From 71c8e9aa9c444bac31a511c0fa481d4703eabd14 Mon Sep 17 00:00:00 2001 From: alxrod Date: Tue, 31 Oct 2023 23:23:22 -0400 Subject: [PATCH 01/59] ready for pr --- images/ondevice_fed_averaging.png | Bin 0 -> 143002 bytes images/ondevice_gboard_approach.png | Bin 0 -> 248245 bytes images/ondevice_gboard_example.png | Bin 0 -> 203911 bytes images/ondevice_intro.png | Bin 0 -> 48584 bytes images/ondevice_medperf.png | Bin 0 -> 287290 bytes images/ondevice_pretraining.png | Bin 0 -> 188591 bytes images/ondevice_pruning.png | Bin 0 -> 110276 bytes images/ondevice_quantization_matrix.png | Bin 0 -> 23462 bytes images/ondevice_split_model.png | Bin 0 -> 204841 bytes images/ondevice_training_flow.png | Bin 0 -> 82838 bytes images/ondevice_transfer_learning_apps.png | Bin 0 -> 403518 bytes images/ondevice_transfer_tinytl.png | Bin 0 -> 151076 bytes ondevice_learning.qmd | 592 ++++++++- references.bib | 1272 ++++++++++++++------ 14 files changed, 1482 insertions(+), 382 deletions(-) create mode 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This contrasts with traditional methods where models are trained on expansive cloud computing resources before deployment. With On-Device Learning, devices like smart speakers, wearables, and industrial sensors can refine models in real-time based on local data, without needing to transmit data externally. For example, a voice-enabled smart speaker could learn and adapt to its owner's speech patterns and vocabulary right on the device. But there is no such thing as free lunch, therefore in this chapter, we will discuss both the benefits and the limitations of on-device learning. -* coming soon. +## Introduction -::: +On-device Learning refers to the process of training machine learning models directly on the device where they are deployed, as opposed to traditional methods where models are trained on powerful servers and then deployed to devices. This method is particularly relevant to TinyML, where machine learning systems are integrated into tiny, resource-constrained devices. -## Introduction +An example of On-Device Learning can be seen in a smart thermostat that adapts to user behavior over time. Initially, the thermostat may have a generic model that understands basic patterns of usage. However, as it is exposed to more data, such as the times the user is home or away, preferred temperatures, and external weather conditions, the thermostat can refine its model directly on the device to provide a personalized experience for the user. This is all done without the need to send data back to a central server for processing. Another example is in predictive text on smartphones. As users type, the phone learns from the user’s language patterns and suggests words or phrases that are likely to be used next. This learning happens directly on the device, and the model updates in real-time as more data is collected. A widely used real-world example of on-device learning is Gboard. On an Android phone, the Gboard keyboard can learn from your keyboard and dictation use to help improve Gboard for everyone. -Explanation: This section sets the stage for the reader, explaining why on-device learning is a critical aspect of embedded AI systems. +![The cycle of federated learning from personal phones to the full-scale model.(Credit: [Google Research](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html))](images/ondevice_intro.png) - - Importance in Embedded AI - - Why is On-device Learning Needed +On-Device Learning offers several advantages. It reduces the need for data transmission, which can be costly and time-consuming. It also addresses privacy concerns, as data does not need to be sent to a central server for processing. Additionally, On-Device Learning can provide a more personalized and responsive user experience, as the model can adapt in real-time to the user’s behavior. ## Advantages and Limitations -Explanation: Understanding the pros and cons of on-device learning helps to identify the scenarios where it is most effective and the challenges that need to be addressed. +On-Device Machine Learning comes with a variety of benefits and a unique set of challenges. This chapter explores the benefits and tradeoffs when working with on-device learning and compares it with traditional cloud-based machine learning. - - Benefits - - Constraints +### Benefits -## Continuous Learning +#### Privacy and Data Security -Explanation: Continuous learning is essential for embedded systems to adapt to new data and situations without requiring frequent updates from a central server. +One of the significant advantages of on-device learning is the enhanced privacy and security of user data. For instance, consider a smartwatch that monitors sensitive health metrics such as heart rate and blood pressure. By processing data and adapting models directly on the device, the biometric data remains localized, circumventing the need to transmit raw data to cloud servers where it could be susceptible to breaches. - - Incremental Algorithms - - Adaptability +Server breaches are far from rare, with millions of records compromised annually. For example, the 2017 Equifax breach exposed the personal data of 147 million people. By keeping data on the device, the risk of such exposures is drastically minimized. On-device learning acts as a safeguard against unauthorized access from various threats, including malicious actors, insider threats, and accidental exposure, by eliminating reliance on centralized cloud storage. -## Federated Machine Learning +Regulations like the Health Insurance Portability and Accountability Act ([HIPAA](https://www.cdc.gov/phlp/publications/topic/hipaa.html)) and the General Data Protection Regulation ([GDPR](https://gdpr.eu/tag/gdpr/)) mandate stringent data privacy requirements that on-device learning adeptly addresses. By ensuring data remains localized and is not transferred to other systems, on-device learning facilitates [compliance with these regulations](https://www.researchgate.net/publication/321515854_The_EU_General_Data_Protection_Regulation_GDPR_A_Practical_Guide). + +On-device learning is not just beneficial for individual users; it has significant implications for organizations and sectors dealing with highly sensitive data. For instance, within the military, on-device learning empowers frontline systems to adapt models and function independently of connections to central servers that could potentially be compromised. By localizing data processing and learning, critical and sensitive information is staunchly protected. + +It is also important in preserving the privacy, security, and regulatory compliance of personal and sensitive data. Training and operating models locally, as opposed to in the cloud, substantially augments privacy measures, ensuring that user data is safeguarded from potential threats. + +#### Regulatory Compliance + +On-device learning helps address major privacy regulations like ([GDPR](https://gdpr.eu/tag/gdpr/)) and [CCPA](https://oag.ca.gov/privacy/ccpa). These regulations require data localization, restricting cross-border data transfers to approved countries with adequate controls. GDPR also mandates privacy by design and consent requirements for data collection. By keeping data processing and model training localized on-device, sensitive user data is not transferred across borders. This avoids major compliance headaches for organizations. + +For example, a healthcare provider monitoring patient vitals with wearables would have to ensure cross-border data transfers comply with HIPAA and GDPR if using the cloud. Determining which country's laws apply and securing approvals for international data flows introduces legal and engineering burdens. With on-device learning, no data leaves the device, simplifying compliance. The time and resources spent on compliance are reduced significantly. + +Industries like healthcare, finance and government with highly regulated data can benefit greatly from on-device learning. By localizing data and learning, regulatory requirements on privacy and data sovereignty are more easily met. On-device solutions provide an efficient way to build compliant AI applications. + +Major privacy regulations impose restrictions on cross-border data movement that on-device learning inherently addresses through localized processing. This reduces the compliance burden for organizations working with regulated data. + +#### Reduced Bandwidth, Costs, and Increased Efficiency + +One major advantage of on-device learning is the significant reduction in bandwidth usage and associated cloud infrastructure costs. By keeping data localized for model training, rather than transmitting raw data to the cloud, on-device learning can result in substantial savings in bandwidth. For instance, a network of cameras analyzing video footage can achieve up to significant reductions in data transfer by training models on-device rather than streaming all video footage to the cloud for processing. + +This reduction in data transmission not only saves bandwidth but also translates to lower costs for servers, networking, and data storage in the cloud. Large organizations, which might spend millions on cloud infrastructure to train models on-device data, can experience dramatic cost reductions through on-device learning. In the era of Generative AI, where costs have been escalating significantly, finding ways to keep expenses down has become increasingly important. + +Furthermore, the energy and environmental costs associated with running large server farms are also diminished. Data centers are known to consume vast amounts of energy, contributing to greenhouse gas emissions. By reducing the need for extensive cloud-based infrastructure, on-device learning plays a part in mitigating the [environmental impact of data processing](http://arxiv.org/abs/2111.00364#). + +Specifically for endpoint applications, on-device learning minimizes the number of network API calls needed to run inference through a cloud provider. For applications with millions of users, the cumulative costs associated with bandwidth and API calls can quickly escalate. In contrast, performing training and inferences locally is considerably more efficient and cost-effective. [On-device learning](http://arxiv.org/abs/2111.00364#) has been shown to reduce training memory requirements, drastically improve memory efficiency, and reduce up to 20% in per-iteration latency under the state-of-the-art optimizations. + +Another key benefit of on-device learning is the potential for Internet of Things devices to continuously adapt their machine-learning model to new data for continuous, lifelong learning. On-device models can quickly become outdated as user behavior, data patterns, and preferences change. Continuous learning enables the model to efficiently adapt to new data and improvements and maintain high model performance over time. + +### Limitations + +While traditional cloud-based machine learning systems have access to nearly endless computing resources, on-device learning is often restricted by the limitations in computational and storage power of the edge device that the model is trained on. By definition, an [edge device](http://arxiv.org/abs/1911.00623) is a device with restrained computing, memory, and energy resources, that cannot be easily increased or decreased. Thus, the reliance on edge devices can restrict the complexity, efficiency, and size of on-device machine learning models. + +#### Compute resources + +Traditional cloud-based machine learning systems utilize large servers with multiple high-end GPUs or TPUs that provide nearly endless computational power and memory. For example, services like AWS EC2 allow configuring clusters of GPU instances for massively parallel training. + +In contrast, on-device learning is restricted by the hardware limitations of the edge device it runs on. Edge devices refer to endpoints like smartphones, embedded electronics, and IoT devices. By definition, these devices have highly restrained computing, memory, and energy resources compared to the cloud. + +For example, a typical smartphone or Raspberry Pi may only have a few CPU cores, a few GB of RAM, and a small battery. These resources are fixed and can't easily be increased on demand like scaling cloud infrastructure. This reliance on edge devices directly restricts the complexity, efficiency, and size of models that can be deployed for on-device training: + +- Complexity: Limits on memory, computing, and power restrict model architecture design, constraining the number of layers and parameters. +- Efficiency: Models must be heavily optimized through methods like quantization and pruning to run faster and consume less energy. +- Size: Actual model files must be compressed as much as possible to fit within the storage limitations of edge devices. + +Thus, while the cloud offers endless scalability, on-device learning must operate within the tight resource constraints of endpoint hardware. This requires careful co-design of streamlined models, training methods, and optimizations tailored specifically for edge devices. + +#### Dataset Size + +In addition to limited computing resources, on-device learning is also constrained in terms of the dataset available for training models. + +In the cloud, models are trained on massive, diverse datasets like ImageNet or Common Crawl. For example, ImageNet contains over 14 million images carefully categorized across thousands of classes. + +On-device learning instead relies on smaller, decentralized data silos unique to each device. A smartphone camera roll may contain only thousands of photos centered around a user's specific interests and environments. + + +This decentralized data leads to a lack of IID (independent and identically distributed) data. For instance, two friends may take many photos of the same places and objects, meaning their data distributions are highly correlated rather than independent. + +Reasons data may be non-IID in on-device settings: + +- User heterogeneity: different users have different interests and environments. +- Device differences: sensors, regions, and demographics affect data. +- Temporal effects: time of day, seasonal impacts on data. + + +The effectiveness of machine learning relies heavily on large, diverse training data. With small, localized datasets, on-device models may fail to generalize across different user populations and environments. + +For example, a disease detection model trained only on images from a single hospital would not generalize well to other patient demographics. Without extensive, diverse medical images, the model's real-world performance would suffer. + +Thus, while cloud-based learning leverages massive datasets, on-device learning relies on much smaller, decentralized data silos unique to each user. This can limit model accuracy and generalization, especially for domain-specific use cases requiring broad training data. + +#### Accuracy & Generalization + +The limited data and optimizations required for on-device learning can negatively impact model accuracy and generalization: +- Small datasets increase overfitting risk. For example, a fruit classifier trained on 100 images risks overfitting compared to one trained on 1 million diverse images. +- Noisy user-generated data reduces quality. Sensor noise or improper data labeling by non-experts may degrade training. +- Optimizations like pruning and quantization trade off accuracy for efficiency. An 8-bit quantized model runs faster but less accurately than a 32-bit model. + +So while cloud models achieve high accuracy with massive datasets and no constraints, on-device models can struggle to generalize. [Some studies](http://arxiv.org/abs/2206.15472) show that on-device training matches cloud accuracy on select tasks. However, performance on real-world workloads requires further study. + +For instance, a cloud model can accurately detect pneumonia in chest X-rays from thousands of hospitals. However, an on-device model trained only on a small local patient population may fail to generalize. + +Unreliable accuracy limits the real-world applicability of on-device learning for mission-critical uses like disease diagnosis or self-driving vehicles. + +On-device training is also slower than the cloud due to limited resources. Even if each iteration is faster, the overall training process takes longer. + +For example, a real-time robotics application may require model updates within milliseconds. On-device training on small embedded hardware may take seconds or minutes per update - too slow for real-time use. + +Accuracy, generalization, and speed challenges pose hurdles to adopting on-device learning for real-world production systems, especially when reliability and low latency are critical. + + +## Ondevice Adaptation + +On-Device Adaptation refers to the process of customizing or updating machine learning models directly on edge or embedded devices. This adaptation allows the models to improve their performance, accuracy, and relevance without relying on external servers or cloud-based resources, enabling the model to adapt and personalize its behavior based on user-specific data and preferences continuously without relying on constant external server communication. On-device adaptation in IoT devices has become increasingly important for privacy, user experience, and offline functionality in various applications. + +In a machine learning task, resource consumption [mainly](http://arxiv.org/abs/1911.00623) comes from three sources: 1: The ML model itself; 2: the optimization process during model learning; 3: storing and processing the dataset used for learning. + +Correspondingly, there are [three](http://arxiv.org/abs/1911.00623) approaches to adapting existing ML algorithms onto resource-constrained devices: +Reducing the complexity of the ML model +Modifying optimizations to reduce training resource requirements +Creating new storage-efficient data representations + +In the following section, we will review these on-device learning adaptation methods. More details on model optimizations can be found in the [Model Optimizations chapter](https://harvard-edge.github.io/cs249r_book/optimizations.html). + +### Reducing Model Complexity +In this section, we will briefly discuss ways to reduce model complexity to adapt ML models on-device. For details of reducing model complexity, please refer to the Model Optimization Chapter. + +#### Traditional ML Algorithms +Due to the compute and memory limitations of edge devices, select traditional ML algorithms are great candidates for on-device learning applications due to their [lightweight nature](http://arxiv.org/abs/1911.00623). Some example algorithms with low resource footprints include Naive Bayes Classifier, Support Vector Machines (SVMs), Linear Regression, Logistic Regression, and select Decision Tree algorithms. With some refinements, these algorithms can be adapted to specific hardware architectures and perform simple tasks, and their low performance requirements make it easy to integrate continuous learning even on edge devices. + +#### Pruning +Pruning is a technique used to reduce the size and complexity of a decision tree or other types of machine learning models, like neural networks, to improve their efficiency and generalization performance. This is beneficial for training models on edge devices, where we want to minimize the resource usage while maintaining competitive accuracy. + +The primary goal of pruning is to remove parts of the model that do not contribute significantly to its predictive power while retaining the most informative aspects. In the context of decision trees, pruning involves removing some of the branches (subtrees) from the tree, leading to a smaller and simpler tree. In the context of DNN, pruning is used to reduce the number of neurons (units) or connections in the network. + +![The network complexity reduces drasticaly after prunning. ([Credit](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*rw2zAHw9Xlm7nSq1PCKbzQ.png))](images/ondevice_pruning.png) + + +#### Reducing Complexity of Deep Learning Models +Traditional cloud-based DNN frameworks have too much memory overhead to be used on-device. [For example](http://arxiv.org/abs/2206.15472), deep learning systems like PyTorch and TensorFlow require hundreds of megabytes of memory overhead when training models such as MobilenetV2-w0.35, and the overhead scales as the number of training parameters increases. + +Current research for lightweight DNNs mostly explore [CNN architectures](http://arxiv.org/abs/1911.00623). Several bare-metal frameworks designed for running Neural Network on MCUs by keeping computational overhead and memory footprint low also exist. Some examples include MNN, TVM, and TensorFlow Lite. However, they can only perform inference during forward pass and lack support for [back-propagation](http://arxiv.org/abs/2206.15472). While these models are designed for edge deployment, their reduction in model weights and architectural connections led to reduced resource requirements for [continuous learning](http://arxiv.org/abs/1911.00623). + +The tradeoff between performance and model support is clear when adapting the most popular DNN systems. How do we adapt existing DNN models to resource-constrained settings while maintaining support for back-propagation and continuous learning? Latest research suggests algorithm and system codesign techniques that help reduce the resource consumption of machine learning training on edge devices. Utilizing techniques such as quantization-aware scaling (QAS), sparse updates, and other cutting edge techniques, on-device learning is possible on embedded systems with a few hundred kilobytes of RAM without additional memory while maintaining [high accuracy](http://arxiv.org/abs/2206.15472). + +### Modifying Optimization Processes + +Choosing the right optimization strategy is important for DNN training on-device, since this allows for the finding of a good local minimum. This optimization strategy must also consider limited memory and power since training occurs on-device. -Explanation: Federated learning allows multiple devices to collaborate in model training without sharing raw data, which is highly relevant for embedded systems concerned with data privacy. +#### Quantization-Aware Scaling + +Quantization is a common method for reducing the memory footprint of DNN training. Although this could introduce new errors, these errors can be mitigated by designing a model to characterize this statistical error. For example, models could use stochastic rounding or introduce the quantization error into the [gradient updates](http://arxiv.org/abs/1911.00623). + +A specific algorithmic technique is Quantization-Aware Scaling (QAS), used to improve the performance of neural networks on low-precision hardware, such as edge devices and mobile devices or TinyML systems, by adjusting the scale factors during the quantization process. + +As you recall from the Optimizations chapter, quantization is the process of mapping a continuous range of values to a discrete set of values. In the context of neural networks, quantization often involves reducing the precision of the weights and activations from 32-bit floating point to lower-precision formats such as 8-bit integers. This reduction in precision can significantly reduce the computational cost and memory footprint of the model, making it suitable for deployment on low-precision hardware. + +![ Quantization reduces complexity by approximating floating point numbers into easier-to-store discrete integers. (Credit: [Nvidia](https://developer-blogs.nvidia.com/wp-content/uploads/2021/07/qat-training-precision.png))](images/ondevice_quantization_matrix.png) + +However, the quantization process can also introduce quantization errors that can degrade the performance of the model. Quantization-aware scaling is a technique that aims to minimize these errors by adjusting the scale factors used in the quantization process. + +The QAS process involves two main steps: + +1. Quantization-aware training: In this step, the neural network is trained with quantization in mind, using simulated quantization to mimic the effects of quantization during the forward and backward passes. This allows the model to learn to compensate for the quantization errors and improve its performance on low-precision hardware. Refer to QAT section in model optimizations for details. +2. Quantization and scaling: After training, the model is quantized to low-precision format, and the scale factors are adjusted to minimize the quantization errors. The scale factors are chosen based on the distribution of the weights and activations in the model, and are adjusted to ensure that the quantized values are within the range of the low-precision format. + +Quantization-aware scaling (QAS) is used to overcome the difficulties of optimizing models on tiny devices. Without needing hyperparamter tuning. QAS automatically scales tensor gradients with various bit-precisions. This in turn stabilizes the training process and matches the accuracy of [floating-point precision](https://arxiv.org/abs/2206.15472). + +#### Sparse Updates +Although QAS enables optimizing a quantized model, it uses a large amount of memory that is unrealistic for on-device training. So spare update is used to reduce the memory footprint of full backward computation. Instead of pruning weights for inference, sparse update prunes the gradient during backwards propagation to update the model sparsely. In other words, [sparse update](https://arxiv.org/abs/2206.15472) skips computing gradients of less important layers and sub-tensors. + +However, determining the optimal sparse update scheme given a constraining memory budget can be challenging due to the large search space. For example, the MCUNet model has 43 convolutional layers and a search space of approximately 1030. One technique to address this issue is contribution analysis. Contribution analysis measures the accuracy improvement from biases (updating the last few biases compared to only updating the classifier) and weights (updating the weight of one extra layer compared to only having a bias update). By trying to maximize these improvements, contribution analysis automatically derives an optimal sparse update scheme for enabling on-device training. + + +#### Layer-Wise Training + +Other methods besides quantization can help optimize routines. One such method is layer-wise training. A significant memory consumer of DNN training is the end-to-end back-propagation. This requires all intermediate feature maps to be stored so the model can calculate gradients. An alternative to this approach that reduces the memory footprint of DNN training is [sequential layer-by-layer training](https://arxiv.org/abs/1604.06174). Instead of training end-to-end, training a single layer at a time helps avoid having to store intermediate feature maps. + +#### Trading Computation for Memory + +The strategy of trading computation for memory involves releasing some of the memory being used to store intermediate results. Instead, these results can be recomputed as needed. [Reducing memory](https://arxiv.org/abs/1606.03401) in exchange for more computation is shown to reduce the memory footprint of DNN training to fit into almost any budget while also minimizing computational cost. + +### Developing New Data Representations + +The dimensionality and volume of the training data can significantly impact on-device adaptation. So another technique for adapting models onto resource-cosntrained devices is to represent datasets in a more efficient way. + +#### Data Compression + +The goal of data compression is to reach high accuracies while limiting the amount of training data. One method to achieve this is prioritizing [sample complexity](https://arxiv.org/abs/1911.00623): the amount of training data required for the algorithm to reach a target accuracy. + +Other more common methods of data compression focus on reducing the dimensionality and the volume of the training data. For example, an approach could take advantage of matrix sparsity to reduce the memory footprint of storing training data. Training data can be transformed into a lower-dimensional embedding and factorized into a dictionary matrix multiplied by a [block-sparse coefficient matrix](https://www.researchgate.net/publication/320089058_TinyDL_Just-in-time_deep_learning_solution_for_constrained_embedded_systems). Another example could involve representing words from a large language training dataset in a more [compressed vector format](https://arxiv.org/abs/1610.09893). - - Architecture - - Optimization ## Transfer Learning -Explanation: Transfer learning enables a pre-trained model to adapt to new tasks with less data, which is beneficial for embedded systems where data might be scarce. +Transfer learning is a machine learning technique where a model developed for a particular task is reused as the starting point for a model on a second task. In the context of on-device AI, transfer learning allows us to leverage pre-trained models that have already learned useful representations from large datasets, and fine-tune them for specific tasks using smaller datasets directly on the device. This can significantly reduce the computational resources and time required for training models from scratch. + +Let's take the example of a smart sensor application that uses on-device AI to recognize objects in images captured by the device. Traditionally, this would require sending the image data to a server, where a large neural network model processes the data and sends back the results. With on-device AI, the model is stored and runs directly on device, eliminating the need to send data to a server. + +If we want to customize the model for the ondevice characteristics, training a neural network model from scratch on the device would however be impractical due to the limited computational resources and battery life. This is where transfer learning comes in. Instead of training a model from scratch, we can take a pre-trained model, such as a convolutional neural network (CNN) or a transformer network that has been trained on a large dataset of images, and fine-tune it for our specific object recognition task. This fine-tuning can be done directly on the device using a smaller dataset of images relevant to the task. By leveraging the pre-trained model, we can reduce the computational resources and time required for training, while still achieving high accuracy for the object recognition task. + +Transfer learning plays an important role in making on-device AI practical by allowing us to leverage pre-trained models and fine-tune them for specific tasks, thereby reducing the computational resources and time required for training. The combination of on-device AI and transfer learning opens up new possibilities for AI applications that are more privacy-conscious and responsive to user needs. + +Transfer learning has revolutionized the way models are developed and deployed, both in the cloud and at the edge. Transfer learning is being used in the real world. One such example is the use of transfer learning to develop AI models that can detect and diagnose diseases from medical images, such as X-rays, MRI scans, and CT scans. For example, researchers at Stanford University have developed a [transfer learning model that can detect cancer in lung X-rays](https://pubmed.ncbi.nlm.nih.gov/28117445/) with an accuracy of 97%. It use in production scenarios can be broadly categorized into two stages: pre-deployment and post-deployment. + +![Transfering knowledge between tasks](images/ondevice_transfer_learning_apps.png) + +### Pre-Deployment Specialization + +In the pre-deployment stage, transfer learning acts as a catalyst to expedite the development process. Here's how it typically works: Imagine we are creating a system to recognize different breeds of dogs. Rather than starting from scratch, we can utilize a pre-trained model that has already mastered the broader task of recognizing animals in images. + +This pre-trained model serves as a solid foundation and contains a wealth of knowledge acquired from extensive data. We then fine-tune this model using a specialized dataset containing images of various dog breeds. This fine-tuning process tailors the model to our specific need – identifying dog breeds with precision. Once fine-tuned and validated to meet performance criteria, this specialized model is then ready for deployment. + +Here's how it works in practice: + +1. Start with a Pre-Trained Model: Begin by selecting a model that has already been trained on a comprehensive dataset, usually related to a general task. This model serves as the foundation for the task at hand. +2. Fine-Tuning: The pre-trained model is then fine-tuned on a smaller, more specialized dataset that is specific to the desired task. This step allows the model to adapt and specialize its knowledge to the specific requirements of the application. +3. Validation: After fine-tuning, the model is validated to ensure it meets the performance criteria for the specialized task. +4. Deployment: Once validated, the specialized model is then deployed into the production environment. + +This method significantly reduces the time and computational resources required to train a model from scratch. By adopting transfer learning, embedded systems can achieve high accuracy on specialized tasks without the need to gather extensive data or expend significant computational resources on training from the ground up. + +### Post-Deployment Adaptation + +Deployment to a device need not mark the culmination of a machine learning (ML) model's educational trajectory. With the advent of transfer learning, we open the doors to the deployment of adaptive ML models to real-world scenarios, catering to the personalized needs of users. + +Consider a real-world application where a parent wishes to identify their child in a collection of images from a school event. In this scenario, the parent is faced with the challenge of locating their child amidst images of many other children. Here, transfer learning can be employed to fine-tune an embedded system's model to this unique and specialized task. Initially, the system might use a generic model trained to recognize faces in images. However, with transfer learning, the system can adapt this model to recognize the specific features of the user's child. + +Here's how it works: + +1. **Data Collection**: The embedded system gathers images that include the child, ideally with the parent's input to ensure accuracy and relevance. This can be done directly on the device, maintaining the user's data privacy. +2. **Model Fine-Tuning**: The pre-existing face recognition model, which has been trained on a large and diverse dataset, is then fine-tuned using the newly collected images of the child. This process adapts the model to recognize the child's specific facial features, distinguishing them from other children in the images. +3. **Validation**: The refined model is then validated to ensure it accurately recognizes the child in various images. This can involve the parent verifying the model's performance and providing feedback for further improvements. +4. **Deployment**: Once validated, the adapted model is deployed on the device, enabling the parent to easily identify their child in images without having to sift through them manually. + +This on-the-fly customization enhances the model's efficacy for the individual user, ensuring that they benefit from ML personalization. This is in part how iPhotos or Google photos works when they ask us to recognize a face and then based on that information they index all the photos by that face. Because the learning and adaptation occur on the device itself, there are no risks to personal privacy. The parent's images are not uploaded to a cloud server or shared with third parties, protecting the family's privacy while still reaping the benefits of a personalized ML model. This approach represents a significant step forward in the quest to provide users with tailored ML solutions that respect and uphold their privacy. + +### Benefits + +Transfer learning has become an important technique in the field of machine learning and artificial intelligence, and it is particularly valuable for several reasons. + +1. **Data Scarcity:** In many real-world scenarios, acquiring a sufficiently large labeled dataset for training a machine learning model from scratch is challenging. Transfer learning mitigates this issue by allowing the use of pre-trained models that have already learned valuable features from a vast dataset. +2. **Computational Expense:** Training a model from scratch requires significant computational resources and time, especially for complex models like deep neural networks. By using transfer learning, we can leverage the computation that has already been done during the training of the source model, thereby saving both time and computational power. +3. **Limited Annotated Data:** For some specific tasks, there might be ample raw data available, but the process of labeling that data for supervised learning can be costly and time-consuming. Transfer learning enables us to utilize pre-trained models that have been trained on a related task with labeled data, hence requiring less annotated data for the new task. + +There are advantages to reusing the features: + +1. **Hierarchical Feature Learning:** Deep learning models, particularly Convolutional Neural Networks (CNNs), have the ability to learn hierarchical features. Lower layers typically learn generic features like edges and shapes, while higher layers learn more complex and task-specific features. Transfer learning allows us to reuse the generic features learned by a model and fine-tune the higher layers for our specific task. +2. **Boosting Performance:** Transfer learning has been proven to boost the performance of models on tasks with limited data. The knowledge gained from the source task can provide a valuable starting point and lead to faster convergence and improved accuracy on the target task. + +### Core Concepts +Understanding the core concepts of transfer learning is essential for effectively utilizing this powerful approach in machine learning. Here we'll break down some of the main principles and components that underlie the process of transfer learning. + +#### Source and Target Tasks + +In transfer learning, there are two main tasks involved: the source task and the target task. The source task is the task for which the model has already been trained and has learned valuable information. The target task is the new task we want the model to perform. The goal of transfer learning is to leverage the knowledge gained from the source task to improve performance on the target task. + +If we have a model trained to recognize various fruits in images (source task), and we want to create a new model to recognize different vegetables in images (target task), we can use transfer learning to leverage the knowledge gained during the fruit recognition task to improve the performance of the vegetable recognition model. + +#### Representation Transfer + +Representation transfer is about transferring the learned representations (features) from the source task to the target task. There are three main types of representation transfer: +- Instance Transfer: This involves reusing the data instances from the source task in the target task. +- Feature-Representation Transfer: This involves transferring the learned feature representations from the source task to the target task. +- Parameter Transfer: This involves transferring the learned parameters (weights) of the model from the source task to the target task. + +In natural language processing, a model trained to understand the syntax and grammar of a language (source task) can have its learned representations transferred to a new model designed to perform sentiment analysis (target task). + +#### Fine-Tuning +Fine-tuning is the process of adjusting the parameters of a pre-trained model to adapt it to the target task. This typically involves updating the weights of the model's layers, especially the last few layers, to make the model more relevant for the new task. In image classification, a model pre-trained on a general dataset like ImageNet (source task) can be fine-tuned by adjusting the weights of its layers to perform well on a specific classification task, like recognizing specific animal species (target task). + +#### Feature Extractions +Feature extraction involves using a pre-trained model as a fixed feature extractor, where the output of the model's intermediate layers is used as features for the target task. This approach is particularly useful when the target task has a small dataset, as the pre-trained model's learned features can significantly enhance performance. In medical image analysis, a model pre-trained on a large dataset of general medical images (source task) can be used as a feature extractor to provide valuable features for a new model designed to recognize specific types of tumors in X-ray images (target task). + +### Types of Transfer Learning + +Transfer learning can be classified into three main types based on the nature of the source and target tasks and data. Let's explore each type in detail: + +#### Inductive Transfer Learning + +In inductive transfer learning, the goal is to learn the target predictive function with the help of source data. It typically involves fine-tuning a pre-trained model on the target task with available labeled data. A common example of inductive transfer learning is image classification tasks. For instance, a model pre-trained on the ImageNet dataset (source task) can be fine-tuned to classify specific types of birds (target task) using a smaller labeled dataset of bird images. + +#### Transductive Transfer Learning + +Transductive transfer learning involves using source and target data, but only the source task. The main aim is to transfer knowledge from the source domain to the target domain, even though the tasks remain the same. Sentiment analysis for different languages can serve as an example of transductive transfer learning. A model trained to perform sentiment analysis in English (source task) can be adapted to perform sentiment analysis in another language, like French (target task), by leveraging parallel datasets of English and French sentences with the same sentiments. + +#### Unsupervised Transfer Learning + +Unsupervised transfer learning is used when the source and target tasks are related, but there is no labeled data available for the target task. The goal is to leverage the knowledge gained from the source task to improve performance on the target task, even without labeled data. An example of unsupervised transfer learning is topic modeling in text data. A model trained to extract topics from news articles (source task) can be adapted to extract topics from social media posts (target task) without needing labeled data for the social media posts. + +#### Comparison and Trade-offs - - Use Cases - - Benefits +By leveraging these different types of transfer learning, practitioners can choose the approach that best fits the nature of their tasks and available data, ultimately leading to more effective and efficient machine learning models. So in summary: +- Inductive: different source and target tasks, different domains +- Transductive: different source and target tasks, same domain +- Unsupervised: unlabeled source data, transfers feature representations -## Data Augmentation +Here's a matrix that outlines in a bit more detail the similarities and differences between the types of transfer learning: -Explanation: Data augmentation can enrich the training set, improving model performance, which is particularly useful when data is limited in embedded systems. +| | Inductive Transfer Learning | Transductive Transfer Learning | Unsupervised Transfer Learning | +|------------------------------|-----------------------------|--------------------------------|---------------------------------| +| **Labeled Data for Target Task** | Required | Not Required | Not Required | +| **Source Task** | Can be different | Same | Same or Different | +| **Target Task** | Can be different | Same | Can be different | +| **Objective** | Improve target task performance with source data | Transfer knowledge from source to target domain | Leverage source task to improve target task performance without labeled data | +| **Example** | ImageNet to bird classification | Sentiment analysis in different languages | Topic modeling for different text data | - - Techniques - - Role in On-Device Learning +### Constraints and Considerations + +When engaging in transfer learning, there are several factors that must be considered to ensure successful knowledge transfer and model performance. Here's a breakdown of some key factors: + +#### Domain Similarity + +Domain similarity refers to how closely related the source and target domains are. The more similar the domains, the more likely the transfer learning will be successful. Transferring knowledge from a model trained on images of outdoor scenes (source domain) to a new task that involves recognizing objects in indoor scenes (target domain) might be more successful than transferring knowledge from outdoor scenes to a task involving text analysis, as the domains (images vs. text) are quite different. + +#### Task Similarity + +Task similarity refers to how closely related the source and target tasks are. Similar tasks are likely to benefit more from transfer learning. A model trained to recognize different breeds of dogs (source task) can be more easily adapted to recognize different breeds of cats (target task) than it can be adapted to perform a completely different task like language translation. + +#### Data Quality and Quantity + +The quality and quantity of data available for the target task can significantly impact the success of transfer learning. More and high-quality data can result in better model performance. If we have a large dataset with clear, well-labeled images for our target task of recognizing specific bird species, the transfer learning process is likely to be more successful than if we have a small, noisy dataset. + +#### Feature Space overlap + +Feature space overlap refers to how well the features learned by the source model align with the features needed for the target task. Greater overlap can lead to more successful transfer learning. A model trained on high-resolution images (source task) may not transfer well to a target task that involves low-resolution images, as the feature space (high-res vs. low-res) is different. + +#### Model Complexity + +The complexity of the source model can also impact the success of transfer learning. Sometimes, a simpler model might transfer better than a complex one, as it is less likely to overfit the source task. A simple convolutional neural network (CNN) model trained on image data (source task) may transfer more successfully to a new image classification task (target task) than a complex CNN with many layers, as the simpler model is less likely to overfit the source task. + +By considering these factors, machine learning practitioners can make informed decisions about when and how to utilize transfer learning, ultimately leading to more successful model performance on the target task. The success of transfer learning hinges on the degree of similarity between the source and target domains. There is risk of overfitting, especially when fine-tuning occurs on a limited dataset. On the computational front, it is worth noting that certain pre-trained models, owing to their size, might not comfortably fit into the memory constraints of some devices or may run prohibitively slowly. Over time, as data evolves, there is potential for model drift, indicating the need for periodic re-training or ongoing adaptation. + +## Federated Machine Learning + +Federated Learning Overview + +The modern internet is full of large networks of connected devices. Whether it’s cell phones, thermostats, smart speakers or any number of other IOT products, countless edge devices are a goldmine for hyper-personalized, rich data. However, with that rich data comes an assortment of problems with information transfer and privacy. Constructing a training dataset in the cloud from these devices would involve high volumes of bandwidth and cost inefficient data transfer and violate user's privacy. + +Federated learning offers a solution to these problems: train models partially on the edge devices and only communicate model updates to the cloud. In 2016, a team from Google designed architecture for federated learning that attempts to address these problems. + +In their initial paper Google outlines a principle federated learning algorithm called FederatedAveraging. Specifically, FederatedAveraging performs stochastic gradient descent (SGD) over several different edge devices. In this process, each device calculates a gradient $g_k = \nabla F_k(w_t)$ which is then applied to update the server side weights as (with $\eta$ as learning rate across $k$ clients): +$$ +w_{t+1} \rightarrow w_t - \eta \sum_{k=1}^{K} \frac{n_k}{n}g_k +$$ +This boils down the basic algorithm for federated learning on the right. For each round of training, the server takes a random set of the client devices and calls each client to train on its local batch using the most recent server side weights. Those weights then get returned to the server where they are collected individually then averaged to update the global model weights. + +![Google's Proposed FederatedAverage Algorithm](images/ondevice_fed_averaging.png) + +With this proposed structure, there are a few key vectors for optimizing federated learning further. We will outline each in the following subsections. + +### Communication Efficiency + +One of the key bottlenecks in federated learning is communication. Every time a client trains the model, they must communicate back to the server their updates. Similarly, once the server has averaged all the updates, it must send them back to the client. On large networks of millions of devices, this incurs huge bandwidth and resource cost. As the field of federated learning advances, a few optimizations have been developed to minimize this communication. To address the footprint of the model, researchers have developed model compression techniques. In the client server protocol, federated learning can also minimize communication through selective sharing of updates on clients. Finally, efficient aggregation techniques can also streamline the communication process + +###Model Compression + +In standard federated learning, the server must communicate the entire model to each client and then the client must send back all of the updated weights. This means that the easiest way to reduce both the memory and communication footprint on the client is to minimize the size of the model needed to be communicated. To do this, we can employ all of the previously discussed model optimization strategies. In 2022, another team at Google proposed [Online Model Compression (OMC)](https://doi.org/10.48550/arXiv.2205.03494) wherein each client communicates via a compressed format and decompresses the model on the fly for training, allocating and deallocating the full memory for the model only for a short period while training. The model is compressed through a range of various quantization strategies elaborated upon in their paper. Meanwhile the server can update the uncompressed model, decompressing and applying updates as they come in. + +### Selective Update Sharing + +There are a breadth of methods for selectively sharing updates. The general principle is that reducing the portion of the model that the clients are training on the edge reduces the memory necessary for training and the size of communication to the server. In basic federated learning, the client trains the entire model. This means that when a client sends an update to the server it has gradients for every weight in the network. However, you cannot just reduce communication by sending pieces of those gradients to the server from each client because the gradients are part of an entire update required to improve the model. Instead, you need to architecturally design the model such that the clients each train only a small portion of the broader model, reducing the total communication while still gaining the benefit of training on client data. A paper from the University of Sheffield applies this concept to a CNN by [splitting the global model into two parts](https://doi.org/10.1145/3517207.3526980): an upper and lower part. +![Split model architecture for selective sharing proposed by [University of Sheffield](https://doi.org/10.1145/3517207.3526980)](images/ondevice_split_model.png) + The lower part is designed to focus on generic features in the dataset while the upper part trained on those generic features is designed to be more sensitive to the activation maps. This means that the lower part of the model is trained through standard federated averaging across all of the clients. Meanwhile, the upper part of the model is trained entirely on the server side from the activation maps generated by the clients. This approach drastically reduces communication for the model while still making the network robust to various types of input found in the data on the client devices. + +### Optimized Aggregation + +In addition to reducing the communication overhead, optimizing the aggregation function can improve model training speed and accuracy in certain federated learning use cases. While the standard for aggregation is just averaging, there are various other approaches which can improve model efficiency, accuracy, and security. One alternative is clipped averaging which clips the model updates within a specific range. Another strategy to preserve security is differential privacy average aggregation. This approach integrates differential privacy into the aggregations tep to protect client identities. Each client adds a layer of random noise to their updates before communicating to the server. The server then updates the server with the noisy updates, meaning that the amount of noise needs to be tuned carefully to balance privacy and accuracy. + +In addition to security enhancing aggregation methods, there are several modifications to the aggregation methods that can improve training speed and performance by adding client metadata along with the weight updates. Momentum aggregation is a technique which helps address the convergence problem. In federated learning, clients data can be extremely heterogeneous depending on the different environments devices are in. That means that many models with heterogeneous data may struggle to converge. Each client stores a momentum term locally which tracks the pace of change over several updates. With clients communicating this momentum, the server can factor in the rate of change of each update when changing the global model to accelerate convergence. Similarly, weighted aggregation can factoro in the client performance or other parameters like device type or network connection strength to adjust the weight with which the server should incorporate the model updates. ([Further description of specific aggregation algorithms](https://doi.org/10.3390/electronics12102287)) + +### Handling non-IID Data + +When using federated learning to train a model across many client devices, it is convenient to consider the data to be independent and identically distributed (IID) across all clients. When data is IID, the model will converge faster and perform better because each local update on any given client is more representative of the broader dataset. This makes aggregation straightforward as you can directly average all clients. However, this is not how data often appears in the real world. Consider a few of the following ways in which data may be non-IID: + +- If you are learning on a set of health-monitor devices, different device models could mean different sensor qualities and properties. This means that low quality sensors and devices may produce data, and therefore model updates distinctly different than high quality ones + +- A smart keyboard trained to perform autocorrect. If you have a disproportionate amount of devices from a certain region the slang, sentence structure, or even language they were using could skew more model updates towards a certain style of typing + +- If you have wildlife sensors in remote areas, connectivity may not be equally distributed causing some clients in certain regions to be able to send more model updates than others. If those regions have different wildlife activity from certain species, that could skew the updates toward those animals + +There are a few approaches to addressing non-IID data in federated learning. One approach would be to change the aggregation algorithm. If you use a weighted aggregation algorithm, you can adjust based on different client properties like region, sensor properties, or connectivity ([Further algorithm description here](https://doi.org/10.48550/arXiv.1806.00582)) + +### Client Selection + +Considering all of the factors influencing the efficacy of federated learning like IID data and communication, client selection is key component to ensuring a system trains well. Selecting the wrong clients can skew the dataset, resulting in non-IID data. Similarly, choosing clients randomly with bad network connections can slow down communication. Therefore, when selecting the right subset of clients, several key characteristics must be considered. + +When selecting clients, there are three main components to consider: data heterogeneity, resource allocation, and communication cost. To address data heterogeneity, we can select for clients on the previously proposed metrics in the non-IID section. In federated learning, all devices may not have the same amount of compute, resulting in some being more inefficient at training than others. When selecting a subset of clients for training, one must consider a balance of data heterogeneity and available resources. In an ideal scenario, you can always select the subset of clients with the greatest resources. However, this may skew your dataset so a balance must be struck. Communication differences add another layer to this, you do not want to be bottlenecked by waiting for devices with poor connections to transmit their entire updates. Therefore, you must also consider choosing a subset of diverse yet well connected devices. + +### An Example of Deployed Federated Learning: G board + +A primary example of a deployed federated learning system is Google’s Keyboard, Gboard, for android devices. In their implementation of federated learning for the keyboard, Google focused on employing differential privacy techniques to protect the user’s data and identity. [Gboard](http://arxiv.org/abs/2305.18465) leverages language models for several key features such as Next Word Prediction (NWP), Smart Compose (SC), and On-The-Fly rescoring (OTF). NWP will anticipate the next word the user is trying to type based on the previous one. SC gives inline suggestions to speed up the typing based on each character. OTF will re-rank the proposed next words based on the active typing process. All three of these models need to run quickly on the edge and federated learning can accelerate training on the users data. However, uploading every word a user typed to the cloud for training would be a massive privacy violation. Therefore, federated learning with an emphasis on differential privacy protects the user while still enabling a better user experience. + +![Examples of Google G Board Features](images/ondevice_gboard_example.png) + +To accomplish this goal, Google employed their algorithm DP-FTRL which provides a formal guarantee that trained models will not memorize specific user data or identities. DP-FTRL combined with secure aggregation, a strategy of encrypting model updates, provides an optimal balance of privacy and utility. Furthermore, adaptive clipping is applied in the aggregation process to limit the impact of individual users on the global model. Through a combination of all of these techniques, Google can continuously refine their keyboard while preserving user privacy in a formally provable way. + +![Google's System Design for Differential Privacy in G Board](images/ondevice_gboard_approach.png) + +### Benchmarking for Federated Learning: MedPerf + +One of the richest examples of data on the edge is medical devices. These devices store some of the most personal data on users but offer huge advances in personalized treatment and better accuracy in medical AI. Given these two factors, medical devices are the perfect use case for federated learning. [MedPerf](https://doi.org/10.1038/s42256-023-00652-2) is an open source platform used to benchmark models using federated evaluation. Instead of just training models via federated learning, MedPerf takes the model to edge devices to test it against personalized data while preserving privacy. In this way a benchmark committee can evaluate various models in the real world on edge devices while still preserving patient anonymity. ## Security Concerns -Explanation: Security is a significant concern for any system that performs learning on-device, as it may expose vulnerabilities. +Performing machine learning model training and adaptation on end-user devices also introduces security risks that must be addressed. Some key security concerns include: + +- **Exposure of private data**: Training data may be leaked or stolen from devices +- **Data poisoning**: Adversaries can manipulate training data to degrade model performance +- **Model extraction**: Attackers may attempt to steal trained model parameters +- **Membership inference**: Models may reveal participation of specific users' data +- **Evasion attacks**: Specially crafted inputs can cause misclassification + +Any system that performs learning on-device introduces security concerns, as it may expose vulnerabilities in larger scale models. There are numerous security risks associated with any machine learning model, but these risks have specific consequences for on-device learning. Fortunately, there are methods to mitigate these risks to improve the real-world performance of on-device learning. + +### Data Poisoning +On-device machine learning introduces unique data security challenges compared to traditional cloud-based training. In particular, data poisoning attacks pose a serious threat during on-device learning. Adversaries can manipulate training data to degrade model performance when deployed. + +Several data poisoning attack techniques exist: + +- Label Flipping involves applying incorrect labels to samples. For instance, in image classification, cat photos may be labeled as dogs to confuse the model. Flipping even [10% of labels](https://proceedings.mlr.press/v139/schwarzschild21a.html) can have significant consequences on the model. +- Data Insertion introduces fake or distorted inputs into the training set. This could include pixelated images, noisy audio, or garbled text. +- Logic Corruption alters the underlying [patterns](https://www.worldscientific.com/doi/10.1142/S0218001414600027) in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more [afraid of data poisoning](https://proceedings.mlr.press/v139/schwarzschild21a.html) than other adversarial machine learning concerns. + +What makes data poisoning alarming is how it exploits the discrepancy between curated datasets and live training data. Consider a cat photo dataset collected from the internet. In the weeks later when this data trains a model on-device, new cat photos on the web differ significantly. + +With data poisoning, attackers purchase domains and upload content that influences a portion of the training data. Even small data changes significantly impact the model's learned behavior. Consequently, poisoning can instill racist, sexist, or other harmful biases if unchecked. + +[Microsoft Tay](https://en.wikipedia.org/wiki/Tay_(chatbot)) was a chatbot launched by Microsoft in 2016. It was designed to learn from its interactions with users on social media platforms like Twitter. Unfortunately, Microsoft Tay became a prime example of data poisoning in machine learning models. Within 24 hours of its launch, Microsoft had to take Tay offline because it had started producing offensive and inappropriate messages, including hate speech and racist comments. This occurred because some users on social media intentionally fed Tay with harmful and offensive input, which the chatbot then learned from and incorporated into its responses. + +This incident is a clear example of data poisoning because malicious actors intentionally manipulated the data used to train and inform the chatbot's responses. The data poisoning resulted in the chatbot adopting harmful biases and producing output that was not intended by its developers. It demonstrates how even small amounts of maliciously crafted data can have a significant impact on the behavior of machine learning models, and highlights the importance of implementing robust data filtering and validation mechanisms to prevent such incidents from occurring. + +Real-world impacts of such biases could be dangerous. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like [Five Safes](https://www.researchgate.net/publication/292975549_Five_Safes_designing_data_access_for_research) ensures models are trained on high-quality, representative data. + +Data poisoning is a pressing concern for secure on-device learning, since data at the end point cannot be easily monitored in real-time and if models are allowed to adapt on their own then we run the risk of the device acting malicously. But continued research in adversarial machine learning aims to develop robust solutions to detect and mitigate such data attacks. + +### Adversarial Attacks + +During the training phase, attackers might inject malicious data into the training dataset, which can subtly alter the model's behavior. For example, an attacker could add images of cats that are labeled as dogs into a dataset used to train an image classification model. If done cleverly, the model's accuracy might not significantly drop, and the attack could go unnoticed. The model would then incorrectly classify some cats as dogs, which could have consequences depending on the application. + +In an embedded security camera system, for instance, this could allow an intruder to avoid detection by wearing a specific pattern that the model has been tricked into classifying as non-threatening. + +During the inference phase, attackers can use adversarial examples to fool the model. Adversarial examples are inputs that have been slightly altered in a way that causes the model to make incorrect predictions. For instance, an attacker might add a small amount of noise to an image in a way that causes a face recognition system to misidentify a person. These attacks can be particularly concerning in applications where safety is at stake, such as autonomous vehicles. In the example you mentioned, the researchers were able to cause a traffic sign recognition system to misclassify a stop sign as a speed sign. This type of misclassification could potentially lead to accidents if it occurred in a real-world autonomous driving system. - - Risks - - Mitigation +To mitigate these risks, several defenses can be employed: + +- Data Validation and Sanitization: Before incorporating new data into the training dataset, it should be thoroughly validated and sanitized to ensure it is not malicious. +- Adversarial Training: The model can be trained on adversarial examples to make it more robust to these types of attacks. +- Input Validation: During inference, inputs should be validated to ensure they have not been manipulated to create adversarial examples. +- Regular Auditing and Monitoring: Regularly auditing and monitoring the model's behavior can help to detect and mitigate adversarial attacks. In the context of tiny ML systems, this is easier said than done, because it is often hard to monitor embedded machine learning systems at the end point due to communication bandwidth limitations and so forth, which we will discuss in the MLOps chapter. + +By understanding the potential risks and implementing these defenses, we can help to secure on-device training at the endpoint/edge and mitigate the impact of adversarial attacks. + +Most people easily confuse data poisoning and adversarial attacks. So here is a table comparing data poisoning and adversarial attacks: + +| Aspect | Data Poisoning | Adversarial Attacks | +|--------------------|-----------------------------------------|-------------------------------------------| +| **Timing** | Training phase | Inference phase | +| **Target** | Training data | Input data | +| **Goal** | Negatively affect model's performance | Cause incorrect predictions | +| **Method** | Insert malicious examples into training data, often with incorrect labels | Add carefully crafted noise to input data | +| **Example** | Adding images of cats labeled as dogs to a dataset used for training an image classification model | Adding a small amount of noise to an image in a way that causes a face recognition system to misidentify a person | +| **Potential Effects** | Model learns incorrect patterns and makes incorrect predictions | Immediate and potentially dangerous incorrect predictions | +| **Applications Affected** | Any machine learning model | Autonomous vehicles, security systems, etc. | + + +### On-Device Learning Security Concerns + +While data poisoning and adversarial attacks are common concerns for machine learning models in general, on-device learning introduces unique security risks. When on-device variants of large-scale models are published, adversaries can exploit these smaller models to attack their larger counterparts. Research has demonstrated that as on-device models and full-scale models become more similar, the vulnerability of the original large-scale models increases significantly. For instance, evaluations across 19 Deep Neural Networks (DNNs) revealed that exploiting on-device models could increase the vulnerability of the original large-scale models by [up to 100 times](http://arxiv.org/abs/2212.13700). + +There are three primary types of security risks specific to on-device learning: + + +- **Transfer-Based Attacks**: These attacks exploit the transferability property between a surrogate model (an approximation of the target model, similar to an on-device model) and a remote target model (the original full-scale model). Attackers generate adversarial examples using the surrogate model, which can then be used to deceive the [target model](http://arxiv.org/abs/2212.13700). For example, imagine an on-device model designed to identify spam emails. An attacker could use this model to generate a spam email that is not detected by the larger, full-scale email filtering system. +- **Optimization-Based Attacks**: These attacks generate adversarial examples for transfer-based attacks using some form of objective function, and iteratively modify inputs to achieve the desired outcome. Gradient estimation attacks, for example, approximate the model’s gradient using query outputs (such as softmax confidence scores), while gradient-free attacks use the model’s final decision (the predicted class) to approximate the gradient, albeit requiring many more [queries](http://arxiv.org/abs/2212.13700). +- **Query Attacks with Transfer Priors**: These attacks combine elements of transfer-based and optimization-based attacks. They reverse engineer on-device models to serve as surrogates for the target full-scale model. In other words, attackers use the smaller on-device model to understand how the larger model works, and then use this knowledge to attack the full-scale model. + +By understanding these specific risks associated with on-device learning, we can develop more robust security protocols to protect both on-device and full-scale models from potential attacks. + +### Mitigation of On-Device Learning Risks + +To mitigate the numerous security risks associated with on-device learning, a variety of methods can be employed. These methods may be specific to the type of attack or serve as a general tool to bolster security. + +One strategy to reduce security risks is to diminish the similarity between on-device models and full-scale models, thereby reducing transferability by up to 90%. This method, known as similarity-unpairing, addresses the problem that arises when adversaries exploit the input-gradient similarity between the two models. By fine-tuning the full-scale model to create a new version with similar accuracy but different input gradients, we can then construct the on-device model by quantizing this updated full-scale model. This unpairing reduces the vulnerability of on-device models by limiting the exposure of the original full-scale model. Importantly, the [order of finetuning and quantization](http://arxiv.org/abs/2212.13700) can be varied while still achieving risk mitigation. + +To tackle data poisoning, it is imperative to source datasets from trusted and reliable [vendors](https://www.eetimes.com/cybersecurity-threats-loom-over-endpoint-ai-systems/?_gl=1%2A17zgs0d%2A_ga%2AMTY0MzA1MTAyNS4xNjk4MDgyNzc1%2A_ga_ZLV02RYCZ8%2AMTY5ODA4Mjc3NS4xLjAuMTY5ODA4Mjc3NS42MC4wLjA). + +In combating adversarial attacks, several strategies can be employed. A proactive approach involves generating adversarial examples and incorporating them into the model's training dataset, thereby fortifying the model against such attacks. Tools like [CleverHans](http://github.com/cleverhans-lab/cleverhans), an open-source training library, are instrumental in creating adversarial examples. [Defense distillation](http://arxiv.org/abs/2212.13700) is another effective strategy, wherein the on-device model outputs probabilities of different classifications rather than definitive decisions, making it more challenging for adversarial examples to exploit the model. + +The theft of intellectual property is another significant concern when deploying on-device models. Intellectual property theft is a concern when deploying on-device models, as adversaries may attempt to reverse-engineer the model to steal the underlying technology. To safeguard against intellectual property theft, the binary executable of the trained model should be stored on a microcontroller unit with encrypted software and secured physical interfaces of the chip. Furthermore, the final dataset used for training the model should be kept [private](https://www.eetimes.com/cybersecurity-threats-loom-over-endpoint-ai-systems/?_gl=1%2A17zgs0d%2A_ga%2AMTY0MzA1MTAyNS4xNjk4MDgyNzc1%2A_ga_ZLV02RYCZ8%2AMTY5ODA4Mjc3NS4xLjAuMTY5ODA4Mjc3NS42MC4wLjA). + +Furthermore, on-device models often utilize well-known or open-source datasets, such as MobileNet. As such, it is vital to maintain the [privacy of the final dataset](http://arxiv.org/abs/2212.13700) used for training the model. Additionally, protecting the data augmentation process and incorporating specific use cases can minimize the risk of reverse-engineering an on-device model. + +Lastly, the Adversarial Threat Landscape for Artificial-Intelligence Systems ([ATLAS](https://atlas.mitre.org/)) serves as a valuable matrix tool that helps assess the risk profile of on-device models, empowering developers to identify and [mitigate](https://www.eetimes.com/cybersecurity-threats-loom-over-endpoint-ai-systems/?_gl=1%2A17zgs0d%2A_ga%2AMTY0MzA1MTAyNS4xNjk4MDgyNzc1%2A_ga_ZLV02RYCZ8%2AMTY5ODA4Mjc3NS4xLjAuMTY5ODA4Mjc3NS42MC4wLjA) potential risks proactively. + +### Securing Training Data +There are a variety of different ways to secure on-device training data. Each of these concepts in itself is really deep and could be worth a class by itself. So here we'll briefly allude to those concepts so you're aware about what to learn further. + +#### Encryption + +Encryption serves as the first line of defense for training data. This involves implementing end-to-end encryption for both local storage on devices and communication channels to prevent unauthorized access to raw training data. Trusted execution environments, such as [Intel SGX](https://www.intel.com/content/www/us/en/architecture-and-technology/software-guard-extensions.html) and [ARM TrustZone](https://www.arm.com/technologies/trustzone-for-cortex-a#:~:text=Arm%20TrustZone%20technology%20offers%20an,trust%20based%20on%20PSA%20guidelines.), are essential for facilitating secure training on encrypted data. + +Additionally, when aggregating updates from multiple devices, [secure multi-party computation protocols](https://papers.nips.cc/paper_files/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html) like [Shamir's Secret Sharing](https://web.mit.edu/6.857/OldStuff/Fall03/ref/Shamir-HowToShareASecret.pdf) can be employed to enhance security. A practical application of this is in collaborative on-device learning, where cryptographic privacy-preserving aggregation of user model updates can be implemented. This technique effectively hides individual user data even during the aggregation phase. + +#### Differential Privacy + +Differential privacy is another crucial strategy for protecting training data. By injecting calibrated statistical noise into the data, we can mask individual records while still extracting valuable [population patterns](https://www.cis.upenn.edu/~aaroth/privacybook.html). Managing the [privacy budget](https://arxiv.org/abs/1607.00133) across multiple training iterations and reducing noise as the model converges is also vital. Methods such as formally provable differential privacy, which may include adding Laplace or Gaussian noise scaled to the dataset's sensitivity, can be employed. + +#### Anomaly Detection + +Anomaly detection plays a pivotal role in identifying and mitigating potential data poisoning attacks. This can be achieved through statistical analyses like Principal Component Analysis (PCA) and clustering, which help to detect deviations in aggregated training data. [Time-series methods](https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/reference/ReferencesPapers.aspx?ReferenceID=1163726) such as Cumulative Sum (CUSUM) charts are useful for identifying shifts indicative of potential poisoning. Comparing current data distributions with previously seen clean data distributions can also help to flag anomalies. Moreover, suspected poisoned batches should be removed from the training update aggregation process. For example, spot checks on subsets of training images on devices can be conducted using photoDNA hashes to identify poisoned inputs. + +#### Input Data Validation + +Lastly, input data validation is essential for ensuring the integrity and validity of input data before it is fed into the training model, thereby protecting against adversarial payloads. Similarity measures, such as cosine distance, can be employed to catch inputs that deviate significantly from the expected distribution. Suspicious inputs that may contain adversarial payloads should be quarantined and sanitized. Furthermore, parser access to training data should be restricted to validated code paths only. Leveraging hardware security features, such as ARM Pointer Authentication, can prevent memory corruption (ARM Limited, 2023). An example of this is implementing input integrity checks on audio training data used by smart speakers before processing by the [speech recognition model](https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-023-00299-2). + +## On-Device Training Frameworks +Embedded inference frameworks like [TF-Lite Micro](https://arxiv.org/abs/2010.08678), [TVM](http://arxiv.org/abs/1802.04799), and [MCUNet](http://arxiv.org/abs/2007.10319) provide a slim runtime for running neural network models on microcontrollers and other resource-constrained devices. However, they don't support on-device training. Training requires its own set of [specialized tools](https://arxiv.org/abs/2206.15472) due to the impact of quantization on gradient calculation and the memory footprint of backpropagation. + +In recent years, there are a handful of tools and frameworks that have started to emerge that enable on-device training, and these include [Tiny Training Engine](https://arxiv.org/abs/2206.15472), [TinyTL](https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html), and [TinyTrain](http://arxiv.org/abs/2307.09988). + +### Tiny Training Engine + +Tiny Training Engine (TTE) uses several techniques to optimize memory usage and speed up the training process. First, TTE performs graph optimization like pruning and sparse updates to reduce memory requirements and accelerate computations. Second, TTE offloads the automatic differentiation to compile time instead of runtime. This significantly reduces overhead during training. + +![An overview of the TTE workflow](images/ondevice_training_flow.png) + +Specifically, TTE follows four main steps: + +- During compile time, TTE traces the forward propagation graph and derives the corresponding backward graph for backpropagation. This allows [differentiation](https://harvard-edge.github.io/cs249r_book/frameworks.html#differentiable-programming) to happen at compile time rather than runtime. +- TTE prunes any nodes representing frozen weights from the backward graph. Frozen weights are weights that are not updated during training to reduce certain neurons' impact. Pruning their nodes saves memory. +- TTE reorders the gradient descent operators to interleave them with the backward pass computations. This scheduling minimizes memory footprints. +- TTE uses code generation to compile the optimized forward and backward graphs, which are then deployed for on-device training. + +### Tiny Transfer Learning + +Tiny Transfer Learning ([TinyTL](https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html)) enables memory-efficient on-device training through a technique called weight freezing. During training, much of the memory bottleneck comes from storing intermediate activations and updating the weights in the neural network. + +To reduce this memory overhead, TinyTL freezes the majority of the weights so they do not need to be updated during training. This eliminates the need to store intermediate activations for frozen parts of the network. TinyTL only fine-tunes the bias terms, which are much smaller than the weights. + +![An overview of the TinyTL workflow](images/ondevice_transfer_tinytl.png) + +Freezing weights is applicable not just to fully-connected layers but also convolutional and normalization layers. However, only adapting the biases limits the model's ability to learn and adapt to new data. + +To increase adaptability without much additional memory, TinyTL uses a small residual learning model. This refines the intermediate feature maps to produce better outputs, even with fixed weights. The residual model introduces minimal overhead - less than 3.8% on top of the base model. + +By freezing most weights TinyTL significantly cuts down memory usage during on-device training. The residual model then allows it to still adapt and learn effectively for the task. The combined approach provides memory-efficient on-device training with minimal impact on model accuracy. + +### Tiny Train + +TinyTrain significantly reduces the time required for on-device training by selectively updating only certain parts of the model. It does this using a technique called task-adaptive sparse updating. + +Based on the user data, memory, and compute available on the device, TinyTrain dynamically chooses which layers of the neural network to update during training. This layer selection is optimized to reduce computation and memory usage while maintaining high accuracy. + +![An overview of the TinyTrain workflow](images/ondevice_pretraining.png) + +More specifically, TinyTrain first does offline pretraining of the model. During pretraining, it not only trains the model on the task data but also meta-trains the model. Meta-training means training the model on metadata about the training process itself. This meta-learning improves the model's ability to adapt accurately even when limited data is available for the target task. + +Then, during the online adaptation stage when the model is being customized on the device, TinyTrain performs task-adaptive sparse updates. Using the criteria around the device's capabilities, it selects only certain layers to update through backpropagation. The layers are chosen to balance accuracy, memory usage, and computation time. + +By sparsely updating layers tailored to the device and task, TinyTrain is able to significantly reduce on-device training time and resource usage. The offline meta-training also improves accuracy when adapting with limited data. Together, these methods enable fast, efficient, and accurate on-device training. + +### Comparison + + +Here is a table summarizing the key similarities and differences between the Tiny Training Engine, TinyTL, and TinyTrain frameworks: + +| Framework | Similarities | Differences | +|-|-|-| +| Tiny Training Engine | - On-device training
    - Optimize memory & computation
    - Leverage pruning, sparsity, etc. | - Traces forward & backward graphs
    - Prunes frozen weights
    - Interleaves backprop & gradients
    - Code generation| +| TinyTL | - On-device training
    - Optimize memory & computation
    - Leverage freezing, sparsity, etc. | - Freezes most weights
    - Only adapts biases
    - Uses residual model | +| TinyTrain | - On-device training
    - Optimize memory & computation
    - Leverage sparsity, etc. | - Meta-training in pretraining
    - Task-adaptive sparse updating
    - Selective layer updating | + +This summarizes the key similarities in their goals and techniques, along with the unique approaches of each framework in the differences column. The table format allows for easy side-by-side comparison. ## Conclusion - - Key Takeaways \ No newline at end of file +The concept of on-device learning is increasingly important for increasing the usability and scalability of TinyML. This chapter has delved into the intricacies of on-device learning, exploring its advantages and limitations, adaptation strategies, key related algorithms and techniques, security implications, and existing and emerging on-device training frameworks. + +On-device learning is, undoubtedly, a groundbreaking paradigm that brings forth numerous advantages. By performing training directly on the edge or endpoint devices, on-device learning obviates the need for continuous cloud connectivity, making it particularly well-suited for IoT and edge computing applications. It comes with benefits such as improved privacy, ease of compliance, resource efficiency. At the same time, on-device learning faces limitations related to hardware constraints, limited data size, and reduced model accuracy and generalization. + +Mechanisms such as reduced model complexity, optimization and data compression techniques, and related learning methods such as transfer learning and federated learning allow models to adapt to learn and evolve under resource constraints, thus serving as the bedrock for effective machine learning on edge devices. + +The critical security concerns in on-device learning highlighted in this chapter, ranging from data poisoning and adversarial attacks to specific risks introduced by on-device learning, must be addressed in real workloads for on-device learning to be a viable paradigm. Effective mitigation strategies, such as data validation, encryption, differential privacy, anomaly detection, and input data validation, are crucial to safeguard on-device learning systems from these threats. + +The emergence of specialized on-device training frameworks like Tiny Training Engine, Tiny Transfer Learning, and Tiny Train presents the practical tools to enable efficient on-device training. These frameworks employ various techniques to optimize memory usage, reduce computational overhead, and streamline the on-device training process. + +In conclusion, on-device learning stands at the forefront of TinyML, promising a future where models can autonomously acquire knowledge and adapt to changing environments on edge devices. The application of on-device learning have the potential to revolutionize various domains, including healthcare, industrial IoT, and smart cities. However, the transformative potential of on-device learning must be balanced with robust security measures to protect against data breaches and adversarial threats. Embracing innovative on-device training frameworks and implementing stringent security protocols are key steps in unlocking the full potential of on-device learning. 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TinyTL Reduce Memory, Not Parameters for Efficient.pdf:/Users/alex/Zotero/storage/J9C8PTCX/Cai et al. - TinyTL Reduce Memory, Not Parameters for Efficient.pdf:application/pdf}, +} + +@incollection{kurkova_survey_2018, + address = {Cham}, + title = {A {Survey} on {Deep} {Transfer} {Learning}}, + volume = {11141}, + isbn = {978-3-030-01423-0 978-3-030-01424-7}, + url = {http://link.springer.com/10.1007/978-3-030-01424-7_27}, + language = {en}, + urldate = {2023-10-26}, + booktitle = {Artificial {Neural} {Networks} and {Machine} {Learning} – {ICANN} 2018}, + publisher = {Springer International Publishing}, + author = {Tan, Chuanqi and Sun, Fuchun and Kong, Tao and Zhang, Wenchang and Yang, Chao and Liu, Chunfang}, + editor = {Kůrková, Věra and Manolopoulos, Yannis and Hammer, Barbara and Iliadis, Lazaros and Maglogiannis, Ilias}, + year = {2018}, + doi = {10.1007/978-3-030-01424-7_27}, + note = {Series Title: Lecture Notes in Computer Science}, + pages = {270--279}, + file = {Tan et al. - 2018 - A Survey on Deep Transfer Learning.pdf:/Users/alex/Zotero/storage/5NZ36SGB/Tan et al. - 2018 - A Survey on Deep Transfer Learning.pdf:application/pdf}, +} + +@misc{lin_-device_2022, + title = {On-{Device} {Training} {Under} {256KB} {Memory}}, + url = {http://arxiv.org/abs/2206.15472}, + language = {en}, + urldate = {2023-10-26}, + publisher = {arXiv}, + author = {Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, + month = nov, + year = {2022}, + note = {arXiv:2206.15472 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + annote = {Comment: NeurIPS 2022}, + file = {Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:/Users/alex/Zotero/storage/GMF6SWGT/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf}, +} + +@misc{cottier_trends_2023, + title = {Trends in the {Dollar} {Training} {Cost} of {Machine} {Learning} {Systems}}, + url = {https://epochai.org/blog/trends-in-the-dollar-training-cost-of-machine-learning-systems}, + journal = {Epoch AI Report}, + author = {Cottier, Ben}, + month = jan, + year = {2023}, +} + +@misc{amodei_ai_2018, + title = {{AI} and {Compute}}, + url = {https://openai.com/research/ai-and-compute}, + journal = {OpenAI Blog}, + author = {Amodei, Dario and Hernandez, Danny}, + month = may, + year = {2018}, +} + +@misc{villalobos_trends_2022, + title = {Trends in {Training} {Dataset} {Sizes}}, + url = {https://epochai.org/blog/trends-in-training-dataset-sizes}, + journal = {Epoch AI}, + author = {Villalobos, Pablo and Ho, Anson}, + month = sep, + year = {2022}, +} + +@misc{sevilla_compute_2022, + title = {Compute {Trends} {Across} {Three} {Eras} of {Machine} {Learning}}, + url = {http://arxiv.org/abs/2202.05924}, + language = {en}, + urldate = {2023-10-25}, + publisher = {arXiv}, + author = {Sevilla, Jaime and Heim, Lennart and Ho, Anson and Besiroglu, Tamay and Hobbhahn, Marius and Villalobos, Pablo}, + month = mar, + year = {2022}, + note = {arXiv:2202.05924 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society}, + file = {Sevilla et al. - 2022 - Compute Trends Across Three Eras of Machine Learni.pdf:/Users/alex/Zotero/storage/24N9RZ72/Sevilla et al. - 2022 - Compute Trends Across Three Eras of Machine Learni.pdf:application/pdf}, +} + +@inproceedings{prakash_cfu_2023, + title = {{CFU} {Playground}: {Full}-{Stack} {Open}-{Source} {Framework} for {Tiny} {Machine} {Learning} ({tinyML}) {Acceleration} on {FPGAs}}, + shorttitle = {{CFU} {Playground}}, + url = {http://arxiv.org/abs/2201.01863}, + doi = {10.1109/ISPASS57527.2023.00024}, + language = {en}, + urldate = {2023-10-25}, + booktitle = {2023 {IEEE} {International} {Symposium} on {Performance} {Analysis} of {Systems} and {Software} ({ISPASS})}, + author = {Prakash, Shvetank and Callahan, Tim and Bushagour, Joseph and Banbury, Colby and Green, Alan V. and Warden, Pete and Ansell, Tim and Reddi, Vijay Janapa}, + month = apr, + year = {2023}, + note = {arXiv:2201.01863 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Hardware Architecture}, + pages = {157--167}, + file = {Prakash et al. - 2023 - CFU Playground Full-Stack Open-Source Framework f.pdf:/Users/alex/Zotero/storage/BZNRIDTL/Prakash et al. - 2023 - CFU Playground Full-Stack Open-Source Framework f.pdf:application/pdf}, +} + + +@misc{villalobos_machine_2022, + title = {Machine {Learning} {Model} {Sizes} and the {Parameter} {Gap}}, + url = {http://arxiv.org/abs/2207.02852}, + language = {en}, + urldate = {2023-10-25}, + publisher = {arXiv}, + author = {Villalobos, Pablo and Sevilla, Jaime and Besiroglu, Tamay and Heim, Lennart and Ho, Anson and Hobbhahn, Marius}, + month = jul, + year = {2022}, + note = {arXiv:2207.02852 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Computation and Language}, + file = {Villalobos et al. - 2022 - Machine Learning Model Sizes and the Parameter Gap.pdf:/Users/alex/Zotero/storage/WW69A82B/Villalobos et al. - 2022 - Machine Learning Model Sizes and the Parameter Gap.pdf:application/pdf}, +} + +@article{jordan_machine_2015, + title = {Machine learning: {Trends}, perspectives, and prospects}, + volume = {349}, + issn = {0036-8075, 1095-9203}, + shorttitle = {Machine learning}, + url = {https://www.science.org/doi/10.1126/science.aaa8415}, + doi = {10.1126/science.aaa8415}, + language = {en}, + number = {6245}, + urldate = {2023-10-25}, + journal = {Science}, + author = {Jordan, M. 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2016 - A survey of transfer learning.pdf:/Users/alex/Zotero/storage/3FN2Y6EA/Weiss et al. - 2016 - A survey of transfer learning.pdf:application/pdf}, +} + +@article{pan_survey_2010, + title = {A {Survey} on {Transfer} {Learning}}, + volume = {22}, + issn = {1041-4347}, + url = {http://ieeexplore.ieee.org/document/5288526/}, + doi = {10.1109/TKDE.2009.191}, + language = {en}, + number = {10}, + urldate = {2023-10-25}, + journal = {IEEE Transactions on Knowledge and Data Engineering}, + author = {Pan, Sinno Jialin and Yang, Qiang}, + month = oct, + year = {2010}, + pages = {1345--1359}, + file = {Pan and Yang - 2010 - A Survey on Transfer Learning.pdf:/Users/alex/Zotero/storage/T3H8E5K8/Pan and Yang - 2010 - A Survey on Transfer Learning.pdf:application/pdf}, +} + +@misc{lin_-device_2022-1, + title = {On-{Device} {Training} {Under} {256KB} {Memory}}, + url = {http://arxiv.org/abs/2206.15472}, + language = {en}, + urldate = {2023-10-25}, + publisher = {arXiv}, + author = {Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, + month = nov, + year = {2022}, + note = {arXiv:2206.15472 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + annote = {Comment: NeurIPS 2022}, + file = {Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:/Users/alex/Zotero/storage/DNIY32R2/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf}, +} + +@misc{beutel_flower_2022, + title = {Flower: {A} {Friendly} {Federated} {Learning} {Research} {Framework}}, + shorttitle = {Flower}, + url = {http://arxiv.org/abs/2007.14390}, + language = {en}, + urldate = {2023-10-25}, + publisher = {arXiv}, + author = {Beutel, Daniel J. and Topal, Taner and Mathur, Akhil and Qiu, Xinchi and Fernandez-Marques, Javier and Gao, Yan and Sani, Lorenzo and Li, Kwing Hei and Parcollet, Titouan and de Gusmão, Pedro Porto Buarque and Lane, Nicholas D.}, + month = mar, + year = {2022}, + note = {arXiv:2007.14390 [cs, stat]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}, + annote = {Comment: Open-Source, mobile-friendly Federated Learning framework}, + file = {Beutel et al. - 2022 - Flower A Friendly Federated Learning Research Fra.pdf:/Users/alex/Zotero/storage/T5PNI5LY/Beutel et al. - 2022 - Flower A Friendly Federated Learning Research Fra.pdf:application/pdf}, +} + +@article{cai_tinytl_nodate-1, + title = {{TinyTL}: {Reduce} {Memory}, {Not} {Parameters} for {Efficient} {On}-{Device} {Learning}}, + language = {en}, + author = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song}, + file = {Cai et al. - TinyTL Reduce Memory, Not Parameters for Efficient.pdf:/Users/alex/Zotero/storage/8LC77S95/Cai et al. - TinyTL Reduce Memory, Not Parameters for Efficient.pdf:application/pdf}, +} + +@article{noauthor_notitle_nodate, +} + +@article{yang_online_nodate, + title = {Online {Model} {Compression} for {Federated} {Learning} with {Large} {Models}}, + author = {Yang, Tien-Ju and Xiao, Yonghui and Motta, Giovanni and Beaufays, Francoise and Mathews, Rajiv and Chen, Mingqing}, +} + +@misc{yang_online_2022, + title = {Online {Model} {Compression} for {Federated} {Learning} with {Large} {Models}}, + url = {http://arxiv.org/abs/2205.03494}, + doi = {10.48550/arXiv.2205.03494}, + urldate = {2023-10-29}, + publisher = {arXiv}, + author = {Yang, Tien-Ju and Xiao, Yonghui and Motta, Giovanni and Beaufays, Françoise and Mathews, Rajiv and Chen, Mingqing}, + month = may, + year = {2022}, + note = {arXiv:2205.03494 [cs]}, + keywords = {Computer Science - Machine Learning}, + annote = {Comment: Submitted to INTERSPEECH 2022}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/9RZ9PAZY/Yang et al. - 2022 - Online Model Compression for Federated Learning wi.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/UPCERAL8/2205.html:text/html}, +} + +@inproceedings{shi_data_2022, + title = {Data {Selection} for {Efficient} {Model} {Update} in {Federated} {Learning}}, + url = {http://arxiv.org/abs/2111.03512}, + doi = {10.1145/3517207.3526980}, + urldate = {2023-10-29}, + booktitle = {Proceedings of the 2nd {European} {Workshop} on {Machine} {Learning} and {Systems}}, + author = {Shi, Hongrui and Radu, Valentin}, + month = apr, + year = {2022}, + note = {arXiv:2111.03512 [cs]}, + keywords = {Computer Science - Machine Learning, I.2.6}, + pages = {72--78}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/N382MADX/Shi and Radu - 2022 - Data Selection for Efficient Model Update in Feder.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/MHHYC84Q/2111.html:text/html}, +} + +@article{moshawrab_reviewing_2023, + title = {Reviewing {Federated} {Learning} {Aggregation} {Algorithms}; {Strategies}, {Contributions}, {Limitations} and {Future} {Perspectives}}, + volume = {12}, + copyright = {http://creativecommons.org/licenses/by/3.0/}, + issn = {2079-9292}, + url = {https://www.mdpi.com/2079-9292/12/10/2287}, + doi = {10.3390/electronics12102287}, + language = {en}, + number = {10}, + urldate = {2023-10-29}, + journal = {Electronics}, + author = {Moshawrab, Mohammad and Adda, Mehdi and Bouzouane, Abdenour and Ibrahim, Hussein and Raad, Ali}, + month = jan, + year = {2023}, + note = {Number: 10 +Publisher: Multidisciplinary Digital Publishing Institute}, + keywords = {aggregation algorithms, collaborative artificial systems, decentralized machine learning, distributed intelligent systems, distributed machine learning, federated learning, federated machine learning, privacy-preserving technology}, + pages = {2287}, + file = {Full Text PDF:/Users/alex/Zotero/storage/CLBRQWDF/Moshawrab et al. - 2023 - Reviewing Federated Learning Aggregation Algorithm.pdf:application/pdf}, +} + +@article{zhao_federated_2018, + title = {Federated {Learning} with {Non}-{IID} {Data}}, + url = {http://arxiv.org/abs/1806.00582}, + doi = {10.48550/arXiv.1806.00582}, + urldate = {2023-10-29}, + author = {Zhao, Yue and Li, Meng and Lai, Liangzhen and Suda, Naveen and Civin, Damon and Chandra, Vikas}, + year = {2018}, + note = {arXiv:1806.00582 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, + file = {arXiv.org Snapshot:/Users/alex/Zotero/storage/2IW5C888/1806.html:text/html;Full Text PDF:/Users/alex/Zotero/storage/SJ2CQ7EJ/Zhao et al. - 2018 - Federated Learning with Non-IID Data.pdf:application/pdf}, +} + +@inproceedings{smestad_systematic_2023, + title = {A {Systematic} {Literature} {Review} on {Client} {Selection} in {Federated} {Learning}}, + url = {http://arxiv.org/abs/2306.04862}, + doi = {10.1145/3593434.3593438}, + language = {en}, + urldate = {2023-10-29}, + booktitle = {Proceedings of the 27th {International} {Conference} on {Evaluation} and {Assessment} in {Software} {Engineering}}, + author = {Smestad, Carl and Li, Jingyue}, + month = jun, + year = {2023}, + note = {arXiv:2306.04862 [cs]}, + keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning}, + pages = {2--11}, + file = {Smestad and Li - 2023 - A Systematic Literature Review on Client Selection.pdf:/Users/alex/Zotero/storage/JP67BCE5/Smestad and Li - 2023 - A Systematic Literature Review on Client Selection.pdf:application/pdf}, +} + +@misc{hard_federated_2019, + title = {Federated {Learning} for {Mobile} {Keyboard} {Prediction}}, + url = {http://arxiv.org/abs/1811.03604}, + doi = {10.48550/arXiv.1811.03604}, + urldate = {2023-10-29}, + publisher = {arXiv}, + author = {Hard, Andrew and Rao, Kanishka and Mathews, Rajiv and Ramaswamy, Swaroop and Beaufays, Françoise and Augenstein, Sean and Eichner, Hubert and Kiddon, Chloé and Ramage, Daniel}, + month = feb, + year = {2019}, + note = {arXiv:1811.03604 [cs]}, + keywords = {Computer Science - Computation and Language}, + annote = {Comment: 7 pages, 4 figures}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/RNQ99QGR/Hard et al. - 2019 - Federated Learning for Mobile Keyboard Prediction.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/DUGFM2DL/1811.html:text/html}, +} + +@misc{xu_federated_2023, + title = {Federated {Learning} of {Gboard} {Language} {Models} with {Differential} {Privacy}}, + url = {http://arxiv.org/abs/2305.18465}, + urldate = {2023-10-29}, + publisher = {arXiv}, + author = {Xu, Zheng and Zhang, Yanxiang and Andrew, Galen and Choquette-Choo, Christopher A. and Kairouz, Peter and McMahan, H. Brendan and Rosenstock, Jesse and Zhang, Yuanbo}, + month = jul, + year = {2023}, + note = {arXiv:2305.18465 [cs]}, + keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning}, + annote = {Comment: ACL industry track; v2 updating SecAgg details}, + file = {arXiv.org Snapshot:/Users/alex/Zotero/storage/R77IA8KD/2305.html:text/html;Full Text PDF:/Users/alex/Zotero/storage/Y5HJ29SD/Xu et al. - 2023 - Federated Learning of Gboard Language Models with .pdf:application/pdf}, +} + +@misc{mcmahan_communication-efficient_2023, + title = {Communication-{Efficient} {Learning} of {Deep} {Networks} from {Decentralized} {Data}}, + url = {http://arxiv.org/abs/1602.05629}, + doi = {10.48550/arXiv.1602.05629}, + urldate = {2023-10-29}, + publisher = {arXiv}, + author = {McMahan, H. Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and Arcas, Blaise Agüera y}, + month = jan, + year = {2023}, + note = {arXiv:1602.05629 [cs]}, + keywords = {Computer Science - Machine Learning}, + annote = {Comment: [v4] Fixes a typo in the FedAvg pseudocode. [v3] Updates the large-scale LSTM experiments, along with other minor changes}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/9SGJLC6C/McMahan et al. - 2023 - Communication-Efficient Learning of Deep Networks .pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/5QY7XZ9I/1602.html:text/html}, +} + + +@article{karargyris_federated_2023-1, + title = {Federated benchmarking of medical artificial intelligence with {MedPerf}}, + volume = {5}, + issn = {2522-5839}, + url = {https://doi.org/10.1038/s42256-023-00652-2}, + doi = {10.1038/s42256-023-00652-2}, + number = {7}, + journal = {Nature Machine Intelligence}, + author = {Karargyris, Alexandros and Umeton, Renato and Sheller, Micah J. and Aristizabal, Alejandro and George, Johnu and Wuest, Anna and Pati, Sarthak and Kassem, Hasan and Zenk, Maximilian and Baid, Ujjwal and Narayana Moorthy, Prakash and Chowdhury, Alexander and Guo, Junyi and Nalawade, Sahil and Rosenthal, Jacob and Kanter, David and Xenochristou, Maria and Beutel, Daniel J. and Chung, Verena and Bergquist, Timothy and Eddy, James and Abid, Abubakar and Tunstall, Lewis and Sanseviero, Omar and Dimitriadis, Dimitrios and Qian, Yiming and Xu, Xinxing and Liu, Yong and Goh, Rick Siow Mong and Bala, Srini and Bittorf, Victor and Puchala, Sreekar Reddy and Ricciuti, Biagio and Samineni, Soujanya and Sengupta, Eshna and Chaudhari, Akshay and Coleman, Cody and Desinghu, Bala and Diamos, Gregory and Dutta, Debo and Feddema, Diane and Fursin, Grigori and Huang, Xinyuan and Kashyap, Satyananda and Lane, Nicholas and Mallick, Indranil and Mascagni, Pietro and Mehta, Virendra and Moraes, Cassiano Ferro and Natarajan, Vivek and Nikolov, Nikola and Padoy, Nicolas and Pekhimenko, Gennady and Reddi, Vijay Janapa and Reina, G. Anthony and Ribalta, Pablo and Singh, Abhishek and Thiagarajan, Jayaraman J. and Albrecht, Jacob and Wolf, Thomas and Miller, Geralyn and Fu, Huazhu and Shah, Prashant and Xu, Daguang and Yadav, Poonam and Talby, David and Awad, Mark M. and Howard, Jeremy P. and Rosenthal, Michael and Marchionni, Luigi and Loda, Massimo and Johnson, Jason M. and Bakas, Spyridon and Mattson, Peter and {FeTS Consortium} and {BraTS-2020 Consortium} and {AI4SafeChole Consortium}}, + month = jul, + year = {2023}, + pages = {799--810}, +} + +@misc{lin_-device_2022-2, + title = {On-{Device} {Training} {Under} {256KB} {Memory}}, + url = {http://arxiv.org/abs/2206.15472}, + doi = {10.48550/arXiv.2206.15472}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, + month = nov, + year = {2022}, + note = {arXiv:2206.15472 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + annote = {Comment: NeurIPS 2022}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/ADXFJT22/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/STS299WY/2206.html:text/html}, +} + +@book{voigt_eu_2017, + title = {The {EU} {General} {Data} {Protection} {Regulation} ({GDPR}): {A} {Practical} {Guide}}, + isbn = {978-3-319-57958-0}, + shorttitle = {The {EU} {General} {Data} {Protection} {Regulation} ({GDPR})}, + author = {Voigt, Paul and Bussche, Axel}, + month = jan, + year = {2017}, + doi = {10.1007/978-3-319-57959-7}, + file = {Submitted Version:/Users/alex/Zotero/storage/BSZHFYUA/Voigt and Bussche - 2017 - The EU General Data Protection Regulation (GDPR) .pdf:application/pdf}, +} + +@misc{noauthor_health_2022, + title = {Health {Insurance} {Portability} and {Accountability} {Act} of 1996 ({HIPAA}) {\textbar} {CDC}}, + url = {https://www.cdc.gov/phlp/publications/topic/hipaa.html}, + language = {en-us}, + urldate = {2023-10-31}, + month = jun, + year = {2022}, +} + +@misc{li_lightrnn_2016, + title = {{LightRNN}: {Memory} and {Computation}-{Efficient} {Recurrent} {Neural} {Networks}}, + shorttitle = {{LightRNN}}, + url = {http://arxiv.org/abs/1610.09893}, + doi = {10.48550/arXiv.1610.09893}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Li, Xiang and Qin, Tao and Yang, Jian and Liu, Tie-Yan}, + month = oct, + year = {2016}, + note = {arXiv:1610.09893 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Computation and Language}, + annote = {Comment: NIPS 2016}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/MKZZ6T4D/Li et al. - 2016 - LightRNN Memory and Computation-Efficient Recurre.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/FR9FYVXJ/1610.html:text/html}, +} + +@misc{gruslys_memory-efficient_2016, + title = {Memory-{Efficient} {Backpropagation} {Through} {Time}}, + url = {http://arxiv.org/abs/1606.03401}, + doi = {10.48550/arXiv.1606.03401}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Gruslys, Audrūnas and Munos, Remi and Danihelka, Ivo and Lanctot, Marc and Graves, Alex}, + month = jun, + year = {2016}, + note = {arXiv:1606.03401 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/7WW7QEYX/Gruslys et al. - 2016 - Memory-Efficient Backpropagation Through Time.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/JTTNS4QF/1606.html:text/html}, +} + +@misc{dhar_-device_2020, + title = {On-{Device} {Machine} {Learning}: {An} {Algorithms} and {Learning} {Theory} {Perspective}}, + shorttitle = {On-{Device} {Machine} {Learning}}, + url = {http://arxiv.org/abs/1911.00623}, + doi = {10.48550/arXiv.1911.00623}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Dhar, Sauptik and Guo, Junyao and Liu, Jiayi and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak}, + month = jul, + year = {2020}, + note = {arXiv:1911.00623 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Statistics - Machine Learning}, + annote = {Comment: Edge Learning, TinyML, Resource Constrained Machine Learning, Deep learning on device, Statistical Learning Theory, 45 pages survey}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/DDMR2GKT/Dhar et al. - 2020 - On-Device Machine Learning An Algorithms and Lear.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/GTFSDPC8/1911.html:text/html}, +} + +@inproceedings{kumar_resource-efficient_2017, + title = {Resource-efficient {Machine} {Learning} in 2 {KB} {RAM} for the {Internet} of {Things}}, + url = {https://proceedings.mlr.press/v70/kumar17a.html}, + language = {en}, + urldate = {2023-10-31}, + booktitle = {Proceedings of the 34th {International} {Conference} on {Machine} {Learning}}, + publisher = {PMLR}, + author = {Kumar, Ashish and Goyal, Saurabh and Varma, Manik}, + month = jul, + year = {2017}, + note = {ISSN: 2640-3498}, + pages = {1935--1944}, + file = {Full Text PDF:/Users/alex/Zotero/storage/UEK447QW/Kumar et al. - 2017 - Resource-efficient Machine Learning in 2 KB RAM fo.pdf:application/pdf}, +} + +@inproceedings{darvish_rouhani_tinydl_2017, + title = {{TinyDL}: {Just}-in-time deep learning solution for constrained embedded systems}, + shorttitle = {{TinyDL}}, + url = {https://ieeexplore.ieee.org/document/8050343}, + doi = {10.1109/ISCAS.2017.8050343}, + urldate = {2023-10-31}, + booktitle = {2017 {IEEE} {International} {Symposium} on {Circuits} and {Systems} ({ISCAS})}, + author = {Darvish Rouhani, Bita and Mirhoseini, Azalia and Koushanfar, Farinaz}, + month = may, + year = {2017}, + note = {ISSN: 2379-447X}, + pages = {1--4}, +} + +@misc{chen_training_2016, + title = {Training {Deep} {Nets} with {Sublinear} {Memory} {Cost}}, + url = {http://arxiv.org/abs/1604.06174}, + doi = {10.48550/arXiv.1604.06174}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Chen, Tianqi and Xu, Bing and Zhang, Chiyuan and Guestrin, Carlos}, + month = apr, + year = {2016}, + note = {arXiv:1604.06174 [cs]}, + keywords = {Computer Science - Machine Learning}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/Y6AF8289/Chen et al. - 2016 - Training Deep Nets with Sublinear Memory Cost.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/IQSRN2VR/1604.html:text/html}, +} + +@misc{kwon_tinytrain_2023-2, + title = {{TinyTrain}: {Deep} {Neural} {Network} {Training} at the {Extreme} {Edge}}, + shorttitle = {{TinyTrain}}, + url = {http://arxiv.org/abs/2307.09988}, + doi = {10.48550/arXiv.2307.09988}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Kwon, Young D. and Li, Rui and Venieris, Stylianos I. and Chauhan, Jagmohan and Lane, Nicholas D. and Mascolo, Cecilia}, + month = jul, + year = {2023}, + note = {arXiv:2307.09988 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/MFSIPQV8/Kwon et al. - 2023 - TinyTrain Deep Neural Network Training at the Ext.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/EKN9EP39/2307.html:text/html}, +} + +@misc{lin_mcunet_2020-1, + title = {{MCUNet}: {Tiny} {Deep} {Learning} on {IoT} {Devices}}, + shorttitle = {{MCUNet}}, + url = {http://arxiv.org/abs/2007.10319}, + doi = {10.48550/arXiv.2007.10319}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Cohn, John and Gan, Chuang and Han, Song}, + month = nov, + year = {2020}, + note = {arXiv:2007.10319 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + annote = {Comment: NeurIPS 2020 (spotlight)}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/HT8ZSR5B/Lin et al. - 2020 - MCUNet Tiny Deep Learning on IoT Devices.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/4QN35SDB/2007.html:text/html}, +} + +@misc{david_tensorflow_2021-1, + title = {{TensorFlow} {Lite} {Micro}: {Embedded} {Machine} {Learning} on {TinyML} {Systems}}, + shorttitle = {{TensorFlow} {Lite} {Micro}}, + url = {http://arxiv.org/abs/2010.08678}, + doi = {10.48550/arXiv.2010.08678}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {David, Robert and Duke, Jared and Jain, Advait and Reddi, Vijay Janapa and Jeffries, Nat and Li, Jian and Kreeger, Nick and Nappier, Ian and Natraj, Meghna and Regev, Shlomi and Rhodes, Rocky and Wang, Tiezhen and Warden, Pete}, + month = mar, + year = {2021}, + note = {arXiv:2010.08678 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/3GD7Q7X6/David et al. - 2021 - TensorFlow Lite Micro Embedded Machine Learning o.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/IX6KB86F/2010.html:text/html}, +} + +@inproceedings{cai_tinytl_2020, + title = {{TinyTL}: {Reduce} {Memory}, {Not} {Parameters} for {Efficient} {On}-{Device} {Learning}}, + volume = {33}, + shorttitle = {{TinyTL}}, + url = {https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html}, + urldate = {2023-10-31}, + booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, + publisher = {Curran Associates, Inc.}, + author = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song}, + year = {2020}, + pages = {11285--11297}, + file = {Full Text PDF:/Users/alex/Zotero/storage/QMYMSJ38/Cai et al. - 2020 - TinyTL Reduce Memory, Not Parameters for Efficien.pdf:application/pdf}, +} + +@misc{chen_tvm_2018-1, + title = {{TVM}: {An} {Automated} {End}-to-{End} {Optimizing} {Compiler} for {Deep} {Learning}}, + shorttitle = {{TVM}}, + url = {http://arxiv.org/abs/1802.04799}, + doi = {10.48550/arXiv.1802.04799}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Chen, Tianqi and Moreau, Thierry and Jiang, Ziheng and Zheng, Lianmin and Yan, Eddie and Cowan, Meghan and Shen, Haichen and Wang, Leyuan and Hu, Yuwei and Ceze, Luis and Guestrin, Carlos and Krishnamurthy, Arvind}, + month = oct, + year = {2018}, + note = {arXiv:1802.04799 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Programming Languages}, + annote = {Comment: Significantly improved version, add automated optimization}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/PJBVNAWB/Chen et al. - 2018 - TVM An Automated End-to-End Optimizing Compiler f.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/GMZTF33Q/1802.html:text/html}, +} + +@article{zhuang_comprehensive_2021-1, + title = {A {Comprehensive} {Survey} on {Transfer} {Learning}}, + volume = {109}, + issn = {1558-2256}, + url = {https://ieeexplore.ieee.org/document/9134370}, + doi = {10.1109/JPROC.2020.3004555}, + number = {1}, + urldate = {2023-10-31}, + journal = {Proceedings of the IEEE}, + author = {Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing}, + month = jan, + year = {2021}, + note = {Conference Name: Proceedings of the IEEE}, + pages = {43--76}, + file = {Submitted Version:/Users/alex/Zotero/storage/Y7G7QZZ2/Zhuang et al. - 2021 - A Comprehensive Survey on Transfer Learning.pdf:application/pdf}, +} + +@article{pan_survey_2010-1, + title = {A {Survey} on {Transfer} {Learning}}, + volume = {22}, + issn = {1558-2191}, + url = {https://ieeexplore.ieee.org/document/5288526}, + doi = {10.1109/TKDE.2009.191}, + number = {10}, + urldate = {2023-10-31}, + journal = {IEEE Transactions on Knowledge and Data Engineering}, + author = {Pan, Sinno Jialin and Yang, Qiang}, + month = oct, + year = {2010}, + note = {Conference Name: IEEE Transactions on Knowledge and Data Engineering}, + pages = {1345--1359}, +} + +@misc{noauthor_california_2018, + title = {California {Consumer} {Privacy} {Act} ({CCPA})}, + url = {https://oag.ca.gov/privacy/ccpa}, + language = {en}, + urldate = {2023-10-31}, + journal = {State of California - Department of Justice - Office of the Attorney General}, + month = oct, + year = {2018}, +} + +@misc{noauthor_gdpr_nodate, + title = {{GDPR} {Archives}}, + url = {https://gdpr.eu/tag/gdpr/}, + language = {en-US}, + urldate = {2023-10-31}, + journal = {GDPR.eu}, + file = {Snapshot:/Users/alex/Zotero/storage/BCK7W267/gdpr.html:text/html}, +} + +@misc{sido_cybersecurity_2022, + title = {Cybersecurity {Threats} {Loom} {Over} {Endpoint} {AI} {Systems}}, + url = {https://www.eetimes.com/cybersecurity-threats-loom-over-endpoint-ai-systems/}, + urldate = {2023-10-31}, + journal = {EE Times}, + author = {Sido, Eldar}, + month = sep, + year = {2022}, + file = {Snapshot:/Users/alex/Zotero/storage/GL9DFRPA/cybersecurity-threats-loom-over-endpoint-ai-systems.html:text/html}, +} + +@article{desai_five_nodate, + title = {Five {Safes}: designing data access for research}, + language = {en}, + author = {Desai, Tanvi and Ritchie, Felix and Welpton, Richard}, + file = {Desai et al. - Five Safes designing data access for research.pdf:/Users/alex/Zotero/storage/4IG3FFGL/Desai et al. - Five Safes designing data access for research.pdf:application/pdf}, +} + +@inproceedings{roli_pattern_2013, + address = {Berlin, Heidelberg}, + series = {Lecture {Notes} in {Computer} {Science}}, + title = {Pattern {Recognition} {Systems} under {Attack}}, + isbn = {978-3-642-41822-8}, + doi = {10.1007/978-3-642-41822-8_1}, + language = {en}, + booktitle = {Progress in {Pattern} {Recognition}, {Image} {Analysis}, {Computer} {Vision}, and {Applications}}, + publisher = {Springer}, + author = {Roli, Fabio and Biggio, Battista and Fumera, Giorgio}, + editor = {Ruiz-Shulcloper, José and Sanniti di Baja, Gabriella}, + year = {2013}, + keywords = {adversarial pattern recognition, biometric authentication, poisoning attacks}, + pages = {1--8}, + file = {Full Text PDF:/Users/alex/Zotero/storage/W36S3FVA/Roli et al. - 2013 - Pattern Recognition Systems under Attack.pdf:application/pdf}, +} + +@misc{noauthor_protecting_nodate, + title = {Protecting {AI} {Models} from “{Data} {Poisoning}” - {IEEE} {Spectrum}}, + url = {https://spectrum.ieee.org/ai-cybersecurity-data-poisoning}, + language = {en}, + urldate = {2023-10-31}, + file = {Snapshot:/Users/alex/Zotero/storage/KJAAC8UK/ai-cybersecurity-data-poisoning.html:text/html}, +} + +@misc{hong_publishing_2022, + title = {Publishing {Efficient} {On}-device {Models} {Increases} {Adversarial} {Vulnerability}}, + url = {http://arxiv.org/abs/2212.13700}, + doi = {10.48550/arXiv.2212.13700}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Hong, Sanghyun and Carlini, Nicholas and Kurakin, Alexey}, + month = dec, + year = {2022}, + note = {arXiv:2212.13700 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Cryptography and Security}, + annote = {Comment: Accepted to IEEE SaTML 2023}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/PDCY2MDP/Hong et al. - 2022 - Publishing Efficient On-device Models Increases Ad.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/8YZVTP2Y/2212.html:text/html}, +} + +@misc{wu_sustainable_2022, + title = {Sustainable {AI}: {Environmental} {Implications}, {Challenges} and {Opportunities}}, + shorttitle = {Sustainable {AI}}, + url = {http://arxiv.org/abs/2111.00364}, + doi = {10.48550/arXiv.2111.00364}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Wu, Carole-Jean and Raghavendra, Ramya and Gupta, Udit and Acun, Bilge and Ardalani, Newsha and Maeng, Kiwan and Chang, Gloria and Behram, Fiona Aga and Huang, James and Bai, Charles and Gschwind, Michael and Gupta, Anurag and Ott, Myle and Melnikov, Anastasia and Candido, Salvatore and Brooks, David and Chauhan, Geeta and Lee, Benjamin and Lee, Hsien-Hsin S. and Akyildiz, Bugra and Balandat, Maximilian and Spisak, Joe and Jain, Ravi and Rabbat, Mike and Hazelwood, Kim}, + month = jan, + year = {2022}, + note = {arXiv:2111.00364 [cs]}, + keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Hardware Architecture}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/4ML6W6PT/Wu et al. - 2022 - Sustainable AI Environmental Implications, Challe.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/K7DGBC6N/2111.html:text/html}, +} + +@misc{lin_-device_2022-4, + title = {On-{Device} {Training} {Under} {256KB} {Memory}}, + url = {http://arxiv.org/abs/2206.15472}, + doi = {10.48550/arXiv.2206.15472}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, + month = nov, + year = {2022}, + note = {arXiv:2206.15472 [cs]}, + keywords = {Computer Science - Computer Vision and Pattern Recognition}, + annote = {Comment: NeurIPS 2022}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/64BR84L4/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/X65HXDGN/2206.html:text/html}, +} + +@misc{dhar_-device_2020-1, + title = {On-{Device} {Machine} {Learning}: {An} {Algorithms} and {Learning} {Theory} {Perspective}}, + shorttitle = {On-{Device} {Machine} {Learning}}, + url = {http://arxiv.org/abs/1911.00623}, + doi = {10.48550/arXiv.1911.00623}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Dhar, Sauptik and Guo, Junyao and Liu, Jiayi and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak}, + month = jul, + year = {2020}, + note = {arXiv:1911.00623 [cs, stat]}, + keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Statistics - Machine Learning}, + annote = {Comment: Edge Learning, TinyML, Resource Constrained Machine Learning, Deep learning on device, Statistical Learning Theory, 45 pages survey}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/ZKW95X4B/Dhar et al. - 2020 - On-Device Machine Learning An Algorithms and Lear.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/5F6QZ2KV/1911.html:text/html}, +} + +@misc{power_grokking_2022, + title = {Grokking: {Generalization} {Beyond} {Overfitting} on {Small} {Algorithmic} {Datasets}}, + shorttitle = {Grokking}, + url = {http://arxiv.org/abs/2201.02177}, + doi = {10.48550/arXiv.2201.02177}, + urldate = {2023-10-31}, + publisher = {arXiv}, + author = {Power, Alethea and Burda, Yuri and Edwards, Harri and Babuschkin, Igor and Misra, Vedant}, + month = jan, + year = {2022}, + note = {arXiv:2201.02177 [cs]}, + keywords = {Computer Science - Machine Learning}, + annote = {Comment: Correspondence to alethea@openai.com. Code available at: https://github.com/openai/grok}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/9P6A876M/Power et al. - 2022 - Grokking Generalization Beyond Overfitting on Sma.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/VTZ6CQ6J/2201.html:text/html}, +} + +@inproceedings{schwarzschild_just_2021, + title = {Just {How} {Toxic} is {Data} {Poisoning}? {A} {Unified} {Benchmark} for {Backdoor} and {Data} {Poisoning} {Attacks}}, + shorttitle = {Just {How} {Toxic} is {Data} {Poisoning}?}, + url = {https://proceedings.mlr.press/v139/schwarzschild21a.html}, + language = {en}, + urldate = {2023-11-01}, + booktitle = {Proceedings of the 38th {International} {Conference} on {Machine} {Learning}}, + publisher = {PMLR}, + author = {Schwarzschild, Avi and Goldblum, Micah and Gupta, Arjun and Dickerson, John P. and Goldstein, Tom}, + month = jul, + year = {2021}, + note = {ISSN: 2640-3498}, + pages = {9389--9398}, + file = {Full Text PDF:/Users/alex/Zotero/storage/NFGNG6F4/Schwarzschild et al. - 2021 - Just How Toxic is Data Poisoning A Unified Benchm.pdf:application/pdf}, +} + +@inproceedings{abadi_deep_2016, + title = {Deep {Learning} with {Differential} {Privacy}}, + url = {http://arxiv.org/abs/1607.00133}, + doi = {10.1145/2976749.2978318}, + urldate = {2023-11-01}, + booktitle = {Proceedings of the 2016 {ACM} {SIGSAC} {Conference} on {Computer} and {Communications} {Security}}, + author = {Abadi, Martín and Chu, Andy and Goodfellow, Ian and McMahan, H. Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li}, + month = oct, + year = {2016}, + note = {arXiv:1607.00133 [cs, stat]}, + keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning}, + pages = {308--318}, + file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/EM89UELI/Abadi et al. - 2016 - Deep Learning with Differential Privacy.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/VNUTTFRG/1607.html:text/html}, +} + +@inproceedings{kairouz_secure_2015, + title = {Secure {Multi}-party {Differential} {Privacy}}, + volume = {28}, + url = {https://papers.nips.cc/paper_files/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html}, + urldate = {2023-11-01}, + booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, + publisher = {Curran Associates, Inc.}, + author = {Kairouz, Peter and Oh, Sewoong and Viswanath, Pramod}, + year = {2015}, + file = {Full Text PDF:/Users/alex/Zotero/storage/96IJF4XC/Kairouz et al. - 2015 - Secure Multi-party Differential Privacy.pdf:application/pdf}, } From 841a2e2493457d4aef12138cdaeb49d1e10e72f2 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Wed, 1 Nov 2023 08:31:54 -0400 Subject: [PATCH 02/59] Making a review pass in the PR, outside of the Google docs --- ondevice_learning.qmd | 88 ++++++++++++++++++++++++------------------- workflow.qmd | 2 +- 2 files changed, 50 insertions(+), 40 deletions(-) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index f7cc0616..5ed5021a 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -1,20 +1,27 @@ # On-Device Learning -On-device Learning represents a significant innovation for TinyML, enabling models to train and update directly on small local devices. This contrasts with traditional methods where models are trained on expansive cloud computing resources before deployment. With On-Device Learning, devices like smart speakers, wearables, and industrial sensors can refine models in real-time based on local data, without needing to transmit data externally. For example, a voice-enabled smart speaker could learn and adapt to its owner's speech patterns and vocabulary right on the device. But there is no such thing as free lunch, therefore in this chapter, we will discuss both the benefits and the limitations of on-device learning. +![_DALL·E 3 Prompt: Illustration of a neural network model represented as a busy construction site, with a diverse group of construction workers, both male and female, of various ethnicities, labeled as 'pruning', 'quantization', and 'sparsity'. They are working together to make the neural network more efficient and smaller, while maintaining high accuracy. The 'pruning' worker, a Hispanic female, is cutting unnecessary connections from the middle of the network. The 'quantization' worker, a Caucasian male, is adjusting or tweaking the weights all over the place. The 'sparsity' worker, an African female, is removing unnecessary nodes to shrink the model. Construction trucks and cranes are in the background, assisting the workers in their tasks. The neural network is visually transforming from a complex and large structure to a more streamlined and smaller one._](./images/cover_model_optimizations.png) + +On-device Learning represents a significant innovation for embedded and edge IoT devices, enabling models to train and update directly on small local devices. This contrasts with traditional methods where models are trained on expansive cloud computing resources before deployment. With On-Device Learning, devices like smart speakers, wearables, and industrial sensors can refine models in real-time based on local data, without needing to transmit data externally. For example, a voice-enabled smart speaker could learn and adapt to its owner's speech patterns and vocabulary right on the device. But there is no such thing as free lunch, therefore in this chapter, we will discuss both the benefits and the limitations of on-device learning. ## Introduction On-device Learning refers to the process of training machine learning models directly on the device where they are deployed, as opposed to traditional methods where models are trained on powerful servers and then deployed to devices. This method is particularly relevant to TinyML, where machine learning systems are integrated into tiny, resource-constrained devices. -An example of On-Device Learning can be seen in a smart thermostat that adapts to user behavior over time. Initially, the thermostat may have a generic model that understands basic patterns of usage. However, as it is exposed to more data, such as the times the user is home or away, preferred temperatures, and external weather conditions, the thermostat can refine its model directly on the device to provide a personalized experience for the user. This is all done without the need to send data back to a central server for processing. Another example is in predictive text on smartphones. As users type, the phone learns from the user’s language patterns and suggests words or phrases that are likely to be used next. This learning happens directly on the device, and the model updates in real-time as more data is collected. A widely used real-world example of on-device learning is Gboard. On an Android phone, the Gboard keyboard can learn from your keyboard and dictation use to help improve Gboard for everyone. +An example of On-Device Learning can be seen in a smart thermostat that adapts to user behavior over time. Initially, the thermostat may have a generic model that understands basic patterns of usage. However, as it is exposed to more data, such as the times the user is home or away, preferred temperatures, and external weather conditions, the thermostat can refine its model directly on the device to provide a personalized experience for the user. This is all done without the need to send data back to a central server for processing. + +Another example is in predictive text on smartphones. As users type, the phone learns from the user’s language patterns and suggests words or phrases that are likely to be used next. This learning happens directly on the device, and the model updates in real-time as more data is collected. A widely used real-world example of on-device learning is Gboard. On an Android phone, the Gboard keyboard can learn from your keyboard and dictation use to help improve Gboard for everyone. ![The cycle of federated learning from personal phones to the full-scale model.(Credit: [Google Research](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html))](images/ondevice_intro.png) -On-Device Learning offers several advantages. It reduces the need for data transmission, which can be costly and time-consuming. It also addresses privacy concerns, as data does not need to be sent to a central server for processing. Additionally, On-Device Learning can provide a more personalized and responsive user experience, as the model can adapt in real-time to the user’s behavior. ## Advantages and Limitations -On-Device Machine Learning comes with a variety of benefits and a unique set of challenges. This chapter explores the benefits and tradeoffs when working with on-device learning and compares it with traditional cloud-based machine learning. +On-Device Learning provides a number of advantages over traditional cloud-based machine learning. By keeping data and models on the device, it eliminates the need for costly data transmission and addresses privacy concerns. This allows for more personalized, responsive experiences as the model can adapt in real-time to user behavior. + +However, On-Device Learning also comes with tradeoffs. The limited compute resources on consumer devices can make it challenging to run complex models locally. Datasets are also more restricted since they consist only of user-generated data from a single device. Additionally, updating models requires pushing out new versions rather than seamless cloud updates. + +On-Device Learning opens up new capabilities by enabling offline AI while maintaining user privacy. But it requires carefully managing model and data complexity within the constraints of consumer devices. Finding the right balance between localization and cloud offloading is key to delivering optimized on-device experiences. ### Benefits @@ -64,9 +71,9 @@ In contrast, on-device learning is restricted by the hardware limitations of the For example, a typical smartphone or Raspberry Pi may only have a few CPU cores, a few GB of RAM, and a small battery. These resources are fixed and can't easily be increased on demand like scaling cloud infrastructure. This reliance on edge devices directly restricts the complexity, efficiency, and size of models that can be deployed for on-device training: -- Complexity: Limits on memory, computing, and power restrict model architecture design, constraining the number of layers and parameters. -- Efficiency: Models must be heavily optimized through methods like quantization and pruning to run faster and consume less energy. -- Size: Actual model files must be compressed as much as possible to fit within the storage limitations of edge devices. +- **Complexity**: Limits on memory, computing, and power restrict model architecture design, constraining the number of layers and parameters. +- **Efficiency**: Models must be heavily optimized through methods like quantization and pruning to run faster and consume less energy. +- **Size**: Actual model files must be compressed as much as possible to fit within the storage limitations of edge devices. Thus, while the cloud offers endless scalability, on-device learning must operate within the tight resource constraints of endpoint hardware. This requires careful co-design of streamlined models, training methods, and optimizations tailored specifically for edge devices. @@ -83,10 +90,9 @@ This decentralized data leads to a lack of IID (independent and identically dist Reasons data may be non-IID in on-device settings: -- User heterogeneity: different users have different interests and environments. -- Device differences: sensors, regions, and demographics affect data. -- Temporal effects: time of day, seasonal impacts on data. - +- **User heterogeneity**: different users have different interests and environments. +- **Device differences:** sensors, regions, and demographics affect data. +- **Temporal effects:** time of day, seasonal impacts on data. The effectiveness of machine learning relies heavily on large, diverse training data. With small, localized datasets, on-device models may fail to generalize across different user populations and environments. @@ -116,22 +122,27 @@ Accuracy, generalization, and speed challenges pose hurdles to adopting on-devic ## Ondevice Adaptation -On-Device Adaptation refers to the process of customizing or updating machine learning models directly on edge or embedded devices. This adaptation allows the models to improve their performance, accuracy, and relevance without relying on external servers or cloud-based resources, enabling the model to adapt and personalize its behavior based on user-specific data and preferences continuously without relying on constant external server communication. On-device adaptation in IoT devices has become increasingly important for privacy, user experience, and offline functionality in various applications. +In a machine learning task, resource consumption [mainly](http://arxiv.org/abs/1911.00623) comes from three sources: -In a machine learning task, resource consumption [mainly](http://arxiv.org/abs/1911.00623) comes from three sources: 1: The ML model itself; 2: the optimization process during model learning; 3: storing and processing the dataset used for learning. +* The ML model itself; +* The optimization process during model learning +* Storing and processing the dataset used for learning. Correspondingly, there are [three](http://arxiv.org/abs/1911.00623) approaches to adapting existing ML algorithms onto resource-constrained devices: -Reducing the complexity of the ML model -Modifying optimizations to reduce training resource requirements -Creating new storage-efficient data representations -In the following section, we will review these on-device learning adaptation methods. More details on model optimizations can be found in the [Model Optimizations chapter](https://harvard-edge.github.io/cs249r_book/optimizations.html). +* Reducing the complexity of the ML model +* Modifying optimizations to reduce training resource requirements +* Creating new storage-efficient data representations + +In the following section, we will review these on-device learning adaptation methods. More details on model optimizations can be found in the [Model Optimizations](https://harvard-edge.github.io/cs249r_book/optimizations.html) chapter. ### Reducing Model Complexity In this section, we will briefly discuss ways to reduce model complexity to adapt ML models on-device. For details of reducing model complexity, please refer to the Model Optimization Chapter. #### Traditional ML Algorithms -Due to the compute and memory limitations of edge devices, select traditional ML algorithms are great candidates for on-device learning applications due to their [lightweight nature](http://arxiv.org/abs/1911.00623). Some example algorithms with low resource footprints include Naive Bayes Classifier, Support Vector Machines (SVMs), Linear Regression, Logistic Regression, and select Decision Tree algorithms. With some refinements, these algorithms can be adapted to specific hardware architectures and perform simple tasks, and their low performance requirements make it easy to integrate continuous learning even on edge devices. +Due to the compute and memory limitations of edge devices, select traditional ML algorithms are great candidates for on-device learning applications due to their [lightweight nature](http://arxiv.org/abs/1911.00623). Some example algorithms with low resource footprints include Naive Bayes Classifier, Support Vector Machines (SVMs), Linear Regression, Logistic Regression, and select Decision Tree algorithms. + +With some refinements, these classical ML algorithms can be adapted to specific hardware architectures and perform simple tasks, and their low performance requirements make it easy to integrate continuous learning even on edge devices. #### Pruning Pruning is a technique used to reduce the size and complexity of a decision tree or other types of machine learning models, like neural networks, to improve their efficiency and generalization performance. This is beneficial for training models on edge devices, where we want to minimize the resource usage while maintaining competitive accuracy. @@ -166,8 +177,9 @@ However, the quantization process can also introduce quantization errors that ca The QAS process involves two main steps: -1. Quantization-aware training: In this step, the neural network is trained with quantization in mind, using simulated quantization to mimic the effects of quantization during the forward and backward passes. This allows the model to learn to compensate for the quantization errors and improve its performance on low-precision hardware. Refer to QAT section in model optimizations for details. -2. Quantization and scaling: After training, the model is quantized to low-precision format, and the scale factors are adjusted to minimize the quantization errors. The scale factors are chosen based on the distribution of the weights and activations in the model, and are adjusted to ensure that the quantized values are within the range of the low-precision format. +* **Quantization-aware training:** In this step, the neural network is trained with quantization in mind, using simulated quantization to mimic the effects of quantization during the forward and backward passes. This allows the model to learn to compensate for the quantization errors and improve its performance on low-precision hardware. Refer to QAT section in model optimizations for details. + +* **Quantization and scaling:** After training, the model is quantized to low-precision format, and the scale factors are adjusted to minimize the quantization errors. The scale factors are chosen based on the distribution of the weights and activations in the model, and are adjusted to ensure that the quantized values are within the range of the low-precision format. Quantization-aware scaling (QAS) is used to overcome the difficulties of optimizing models on tiny devices. Without needing hyperparamter tuning. QAS automatically scales tensor gradients with various bit-precisions. This in turn stabilizes the training process and matches the accuracy of [floating-point precision](https://arxiv.org/abs/2206.15472). @@ -218,10 +230,10 @@ This pre-trained model serves as a solid foundation and contains a wealth of kno Here's how it works in practice: -1. Start with a Pre-Trained Model: Begin by selecting a model that has already been trained on a comprehensive dataset, usually related to a general task. This model serves as the foundation for the task at hand. -2. Fine-Tuning: The pre-trained model is then fine-tuned on a smaller, more specialized dataset that is specific to the desired task. This step allows the model to adapt and specialize its knowledge to the specific requirements of the application. -3. Validation: After fine-tuning, the model is validated to ensure it meets the performance criteria for the specialized task. -4. Deployment: Once validated, the specialized model is then deployed into the production environment. +* **Start with a Pre-Trained Model:** Begin by selecting a model that has already been trained on a comprehensive dataset, usually related to a general task. This model serves as the foundation for the task at hand. +* **Fine-Tuning:** The pre-trained model is then fine-tuned on a smaller, more specialized dataset that is specific to the desired task. This step allows the model to adapt and specialize its knowledge to the specific requirements of the application. +* **Validation:** After fine-tuning, the model is validated to ensure it meets the performance criteria for the specialized task. +* **Deployment:** Once validated, the specialized model is then deployed into the production environment. This method significantly reduces the time and computational resources required to train a model from scratch. By adopting transfer learning, embedded systems can achieve high accuracy on specialized tasks without the need to gather extensive data or expend significant computational resources on training from the ground up. @@ -233,10 +245,10 @@ Consider a real-world application where a parent wishes to identify their child Here's how it works: -1. **Data Collection**: The embedded system gathers images that include the child, ideally with the parent's input to ensure accuracy and relevance. This can be done directly on the device, maintaining the user's data privacy. -2. **Model Fine-Tuning**: The pre-existing face recognition model, which has been trained on a large and diverse dataset, is then fine-tuned using the newly collected images of the child. This process adapts the model to recognize the child's specific facial features, distinguishing them from other children in the images. -3. **Validation**: The refined model is then validated to ensure it accurately recognizes the child in various images. This can involve the parent verifying the model's performance and providing feedback for further improvements. -4. **Deployment**: Once validated, the adapted model is deployed on the device, enabling the parent to easily identify their child in images without having to sift through them manually. +1. **Data Collection:** The embedded system gathers images that include the child, ideally with the parent's input to ensure accuracy and relevance. This can be done directly on the device, maintaining the user's data privacy. +2. **Model Fine-Tuning:** The pre-existing face recognition model, which has been trained on a large and diverse dataset, is then fine-tuned using the newly collected images of the child. This process adapts the model to recognize the child's specific facial features, distinguishing them from other children in the images. +3. **Validation:** The refined model is then validated to ensure it accurately recognizes the child in various images. This can involve the parent verifying the model's performance and providing feedback for further improvements. +4. **Deployment:** Once validated, the adapted model is deployed on the device, enabling the parent to easily identify their child in images without having to sift through them manually. This on-the-fly customization enhances the model's efficacy for the individual user, ensuring that they benefit from ML personalization. This is in part how iPhotos or Google photos works when they ask us to recognize a face and then based on that information they index all the photos by that face. Because the learning and adaptation occur on the device itself, there are no risks to personal privacy. The parent's images are not uploaded to a cloud server or shared with third parties, protecting the family's privacy while still reaping the benefits of a personalized ML model. This approach represents a significant step forward in the quest to provide users with tailored ML solutions that respect and uphold their privacy. @@ -358,7 +370,7 @@ With this proposed structure, there are a few key vectors for optimizing federat One of the key bottlenecks in federated learning is communication. Every time a client trains the model, they must communicate back to the server their updates. Similarly, once the server has averaged all the updates, it must send them back to the client. On large networks of millions of devices, this incurs huge bandwidth and resource cost. As the field of federated learning advances, a few optimizations have been developed to minimize this communication. To address the footprint of the model, researchers have developed model compression techniques. In the client server protocol, federated learning can also minimize communication through selective sharing of updates on clients. Finally, efficient aggregation techniques can also streamline the communication process -###Model Compression +### Model Compression In standard federated learning, the server must communicate the entire model to each client and then the client must send back all of the updated weights. This means that the easiest way to reduce both the memory and communication footprint on the client is to minimize the size of the model needed to be communicated. To do this, we can employ all of the previously discussed model optimization strategies. In 2022, another team at Google proposed [Online Model Compression (OMC)](https://doi.org/10.48550/arXiv.2205.03494) wherein each client communicates via a compressed format and decompresses the model on the fly for training, allocating and deallocating the full memory for the model only for a short period while training. The model is compressed through a range of various quantization strategies elaborated upon in their paper. Meanwhile the server can update the uncompressed model, decompressing and applying updates as they come in. @@ -423,9 +435,9 @@ On-device machine learning introduces unique data security challenges compared t Several data poisoning attack techniques exist: -- Label Flipping involves applying incorrect labels to samples. For instance, in image classification, cat photos may be labeled as dogs to confuse the model. Flipping even [10% of labels](https://proceedings.mlr.press/v139/schwarzschild21a.html) can have significant consequences on the model. -- Data Insertion introduces fake or distorted inputs into the training set. This could include pixelated images, noisy audio, or garbled text. -- Logic Corruption alters the underlying [patterns](https://www.worldscientific.com/doi/10.1142/S0218001414600027) in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more [afraid of data poisoning](https://proceedings.mlr.press/v139/schwarzschild21a.html) than other adversarial machine learning concerns. +* **Label Flipping:** It involves applying incorrect labels to samples. For instance, in image classification, cat photos may be labeled as dogs to confuse the model. Flipping even [10% of labels](https://proceedings.mlr.press/v139/schwarzschild21a.html) can have significant consequences on the model. +* **Data Insertion:** It introduces fake or distorted inputs into the training set. This could include pixelated images, noisy audio, or garbled text. +* **Logic Corruption:** This alters the underlying [patterns](https://www.worldscientific.com/doi/10.1142/S0218001414600027) in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more [afraid of data poisoning](https://proceedings.mlr.press/v139/schwarzschild21a.html) than other adversarial machine learning concerns. What makes data poisoning alarming is how it exploits the discrepancy between curated datasets and live training data. Consider a cat photo dataset collected from the internet. In the weeks later when this data trains a model on-device, new cat photos on the web differ significantly. @@ -449,14 +461,12 @@ During the inference phase, attackers can use adversarial examples to fool the m To mitigate these risks, several defenses can be employed: -- Data Validation and Sanitization: Before incorporating new data into the training dataset, it should be thoroughly validated and sanitized to ensure it is not malicious. -- Adversarial Training: The model can be trained on adversarial examples to make it more robust to these types of attacks. -- Input Validation: During inference, inputs should be validated to ensure they have not been manipulated to create adversarial examples. -- Regular Auditing and Monitoring: Regularly auditing and monitoring the model's behavior can help to detect and mitigate adversarial attacks. In the context of tiny ML systems, this is easier said than done, because it is often hard to monitor embedded machine learning systems at the end point due to communication bandwidth limitations and so forth, which we will discuss in the MLOps chapter. - -By understanding the potential risks and implementing these defenses, we can help to secure on-device training at the endpoint/edge and mitigate the impact of adversarial attacks. +* **Data Validation and Sanitization:** Before incorporating new data into the training dataset, it should be thoroughly validated and sanitized to ensure it is not malicious. +* **Adversarial Training:** The model can be trained on adversarial examples to make it more robust to these types of attacks. +* **Input Validation:** During inference, inputs should be validated to ensure they have not been manipulated to create adversarial examples. +* **Regular Auditing and Monitoring:** Regularly auditing and monitoring the model's behavior can help to detect and mitigate adversarial attacks. In the context of tiny ML systems, this is easier said than done, because it is often hard to monitor embedded machine learning systems at the end point due to communication bandwidth limitations and so forth, which we will discuss in the MLOps chapter. -Most people easily confuse data poisoning and adversarial attacks. So here is a table comparing data poisoning and adversarial attacks: +By understanding the potential risks and implementing these defenses, we can help to secure on-device training at the endpoint/edge and mitigate the impact of adversarial attacks. Most people easily confuse data poisoning and adversarial attacks. So here is a table comparing data poisoning and adversarial attacks: | Aspect | Data Poisoning | Adversarial Attacks | |--------------------|-----------------------------------------|-------------------------------------------| diff --git a/workflow.qmd b/workflow.qmd index 1031b77a..36a18c77 100644 --- a/workflow.qmd +++ b/workflow.qmd @@ -1,6 +1,6 @@ # AI Workflow -![_DALL·E 3 Prompt: Illustration in a rectangular format of a stylized flowchart representing the AI workflow chapter. Starting from the left, the stages include 'Data Collection' represented by a database icon, 'Data Preprocessing' with a filter icon, 'Model Design' with a brain icon, 'Training' with a weight icon, 'Evaluation' with a checkmark, and 'Deployment' with a rocket on the far right. Arrows connect each stage, guiding the viewer horizontally through the AI processes, emphasizing the sequential and interconnected nature of these steps._](./images/cover_ai_workflow.png) +![_DALL·E 3 Prompt: Create a rectangular illustration of a stylized flowchart representing the AI workflow/pipeline. From left to right, depict the stages as follows: ‘Data Collection’ with a database icon, ‘Data Preprocessing’ with a filter icon, ‘Model Design’ with a brain icon, ‘Training’ with a weight icon, ‘Evaluation’ with a checkmark, and ‘Deployment’ with a rocket. Connect each stage with arrows to guide the viewer horizontally through the AI processes, emphasizing the sequential and interconnected nature of these steps._](./images/cover_ai_workflow.png) In this chapter, we'll explore the machine learning (ML) workflow, setting the stage for subsequent chapters that delve into the specifics. To ensure we don't lose sight of the bigger picture, this chapter offers a high-level overview of the steps involved in the ML workflow. From a0e0f144f9f62f709208d4bbb117c2cec900aeb3 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Wed, 1 Nov 2023 08:36:37 -0400 Subject: [PATCH 03/59] Minor tweaking --- ondevice_learning.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index 5ed5021a..61cf1ee8 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -590,9 +590,9 @@ This summarizes the key similarities in their goals and techniques, along with t ## Conclusion -The concept of on-device learning is increasingly important for increasing the usability and scalability of TinyML. This chapter has delved into the intricacies of on-device learning, exploring its advantages and limitations, adaptation strategies, key related algorithms and techniques, security implications, and existing and emerging on-device training frameworks. +The concept of on-device learning is increasingly important for increasing the usability and scalability of TinyML. This chapter explored the intricacies of on-device learning, exploring its advantages and limitations, adaptation strategies, key related algorithms and techniques, security implications, and existing and emerging on-device training frameworks. -On-device learning is, undoubtedly, a groundbreaking paradigm that brings forth numerous advantages. By performing training directly on the edge or endpoint devices, on-device learning obviates the need for continuous cloud connectivity, making it particularly well-suited for IoT and edge computing applications. It comes with benefits such as improved privacy, ease of compliance, resource efficiency. At the same time, on-device learning faces limitations related to hardware constraints, limited data size, and reduced model accuracy and generalization. +On-device learning is, undoubtedly, a groundbreaking paradigm that brings forth numerous advantages for embedded and edge ML deployments. By performing training directly on the endpoint devices, on-device learning obviates the need for continuous cloud connectivity, making it particularly well-suited for IoT and edge computing applications. It comes with benefits such as improved privacy, ease of compliance, resource efficiency. At the same time, on-device learning faces limitations related to hardware constraints, limited data size, and reduced model accuracy and generalization. Mechanisms such as reduced model complexity, optimization and data compression techniques, and related learning methods such as transfer learning and federated learning allow models to adapt to learn and evolve under resource constraints, thus serving as the bedrock for effective machine learning on edge devices. From 5fdabf34918deb9f421ff9517c74709571beea81 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Wed, 1 Nov 2023 11:03:01 -0400 Subject: [PATCH 04/59] Writing update --- ondevice_learning.qmd | 2 -- 1 file changed, 2 deletions(-) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index 61cf1ee8..0664a46b 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -586,8 +586,6 @@ Here is a table summarizing the key similarities and differences between the Tin | TinyTL | - On-device training
    - Optimize memory & computation
    - Leverage freezing, sparsity, etc. | - Freezes most weights
    - Only adapts biases
    - Uses residual model | | TinyTrain | - On-device training
    - Optimize memory & computation
    - Leverage sparsity, etc. | - Meta-training in pretraining
    - Task-adaptive sparse updating
    - Selective layer updating | -This summarizes the key similarities in their goals and techniques, along with the unique approaches of each framework in the differences column. The table format allows for easy side-by-side comparison. - ## Conclusion The concept of on-device learning is increasingly important for increasing the usability and scalability of TinyML. This chapter explored the intricacies of on-device learning, exploring its advantages and limitations, adaptation strategies, key related algorithms and techniques, security implications, and existing and emerging on-device training frameworks. From d60b07b5a53b43c23d17eda89a25b8b9eedd19ce Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Wed, 1 Nov 2023 11:42:45 -0400 Subject: [PATCH 05/59] Cover iamge --- ondevice_learning.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index 0664a46b..4986f2fa 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -1,6 +1,6 @@ # On-Device Learning -![_DALL·E 3 Prompt: Illustration of a neural network model represented as a busy construction site, with a diverse group of construction workers, both male and female, of various ethnicities, labeled as 'pruning', 'quantization', and 'sparsity'. They are working together to make the neural network more efficient and smaller, while maintaining high accuracy. The 'pruning' worker, a Hispanic female, is cutting unnecessary connections from the middle of the network. The 'quantization' worker, a Caucasian male, is adjusting or tweaking the weights all over the place. The 'sparsity' worker, an African female, is removing unnecessary nodes to shrink the model. Construction trucks and cranes are in the background, assisting the workers in their tasks. The neural network is visually transforming from a complex and large structure to a more streamlined and smaller one._](./images/cover_model_optimizations.png) +![_DALL·E 3 Prompt: Drawing of a smartphone with its internal components exposed, revealing diverse miniature engineers of different genders and skin tones actively working on the machine learning model. The engineers, including men, women, and non-binary individuals, are tuning parameters, repairing connections, and enhancing the network on the fly. Data flows into the machine learning model, being processed in real-time, and generating output inferences._](./images/cover_ondevice_learning.png) On-device Learning represents a significant innovation for embedded and edge IoT devices, enabling models to train and update directly on small local devices. This contrasts with traditional methods where models are trained on expansive cloud computing resources before deployment. With On-Device Learning, devices like smart speakers, wearables, and industrial sensors can refine models in real-time based on local data, without needing to transmit data externally. For example, a voice-enabled smart speaker could learn and adapt to its owner's speech patterns and vocabulary right on the device. But there is no such thing as free lunch, therefore in this chapter, we will discuss both the benefits and the limitations of on-device learning. From 7d2528ff8b2b17aa1833ff661ef23c793c3a75e5 Mon Sep 17 00:00:00 2001 From: alxrod Date: Wed, 1 Nov 2023 21:42:43 -0400 Subject: [PATCH 06/59] removing old refs, in process of replacing with new ones --- references.bib | 584 ------------------------------------------------- 1 file changed, 584 deletions(-) diff --git a/references.bib b/references.bib index eb6b0c20..ec35ec3a 100644 --- a/references.bib +++ b/references.bib @@ -966,587 +966,3 @@ @misc{lin_-device_2022-1 annote = {Comment: NeurIPS 2022}, file = {Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:/Users/alex/Zotero/storage/DNIY32R2/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf}, } - -@misc{beutel_flower_2022, - title = {Flower: {A} {Friendly} {Federated} {Learning} {Research} {Framework}}, - shorttitle = {Flower}, - url = {http://arxiv.org/abs/2007.14390}, - language = {en}, - urldate = {2023-10-25}, - publisher = {arXiv}, - author = {Beutel, Daniel J. and Topal, Taner and Mathur, Akhil and Qiu, Xinchi and Fernandez-Marques, Javier and Gao, Yan and Sani, Lorenzo and Li, Kwing Hei and Parcollet, Titouan and de Gusmão, Pedro Porto Buarque and Lane, Nicholas D.}, - month = mar, - year = {2022}, - note = {arXiv:2007.14390 [cs, stat]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning}, - annote = {Comment: Open-Source, mobile-friendly Federated Learning framework}, - file = {Beutel et al. - 2022 - Flower A Friendly Federated Learning Research Fra.pdf:/Users/alex/Zotero/storage/T5PNI5LY/Beutel et al. - 2022 - Flower A Friendly Federated Learning Research Fra.pdf:application/pdf}, -} - -@article{cai_tinytl_nodate-1, - title = {{TinyTL}: {Reduce} {Memory}, {Not} {Parameters} for {Efficient} {On}-{Device} {Learning}}, - language = {en}, - author = {Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song}, - file = {Cai et al. - TinyTL Reduce Memory, Not Parameters for Efficient.pdf:/Users/alex/Zotero/storage/8LC77S95/Cai et al. - TinyTL Reduce Memory, Not Parameters for Efficient.pdf:application/pdf}, -} - -@article{noauthor_notitle_nodate, -} - -@article{yang_online_nodate, - title = {Online {Model} {Compression} for {Federated} {Learning} with {Large} {Models}}, - author = {Yang, Tien-Ju and Xiao, Yonghui and Motta, Giovanni and Beaufays, Francoise and Mathews, Rajiv and Chen, Mingqing}, -} - -@misc{yang_online_2022, - title = {Online {Model} {Compression} for {Federated} {Learning} with {Large} {Models}}, - url = {http://arxiv.org/abs/2205.03494}, - doi = {10.48550/arXiv.2205.03494}, - urldate = {2023-10-29}, - publisher = {arXiv}, - author = {Yang, Tien-Ju and Xiao, Yonghui and Motta, Giovanni and Beaufays, Françoise and Mathews, Rajiv and Chen, Mingqing}, - month = may, - year = {2022}, - note = {arXiv:2205.03494 [cs]}, - keywords = {Computer Science - Machine Learning}, - annote = {Comment: Submitted to INTERSPEECH 2022}, - file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/9RZ9PAZY/Yang et al. - 2022 - Online Model Compression for Federated Learning wi.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/UPCERAL8/2205.html:text/html}, -} - -@inproceedings{shi_data_2022, - title = {Data {Selection} for {Efficient} {Model} {Update} in {Federated} {Learning}}, - url = {http://arxiv.org/abs/2111.03512}, - doi = {10.1145/3517207.3526980}, - urldate = {2023-10-29}, - booktitle = {Proceedings of the 2nd {European} {Workshop} on {Machine} {Learning} and {Systems}}, - author = {Shi, Hongrui and Radu, Valentin}, - month = apr, - year = {2022}, - note = {arXiv:2111.03512 [cs]}, - keywords = {Computer Science - Machine Learning, I.2.6}, - pages = {72--78}, - file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/N382MADX/Shi and Radu - 2022 - Data Selection for Efficient Model Update in Feder.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/MHHYC84Q/2111.html:text/html}, -} - -@article{moshawrab_reviewing_2023, - title = {Reviewing {Federated} {Learning} {Aggregation} {Algorithms}; 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urldate = {2023-10-31}, - publisher = {arXiv}, - author = {Wu, Carole-Jean and Raghavendra, Ramya and Gupta, Udit and Acun, Bilge and Ardalani, Newsha and Maeng, Kiwan and Chang, Gloria and Behram, Fiona Aga and Huang, James and Bai, Charles and Gschwind, Michael and Gupta, Anurag and Ott, Myle and Melnikov, Anastasia and Candido, Salvatore and Brooks, David and Chauhan, Geeta and Lee, Benjamin and Lee, Hsien-Hsin S. and Akyildiz, Bugra and Balandat, Maximilian and Spisak, Joe and Jain, Ravi and Rabbat, Mike and Hazelwood, Kim}, - month = jan, - year = {2022}, - note = {arXiv:2111.00364 [cs]}, - keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Hardware Architecture}, - file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/4ML6W6PT/Wu et al. - 2022 - Sustainable AI Environmental Implications, Challe.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/K7DGBC6N/2111.html:text/html}, -} - -@misc{lin_-device_2022-4, - title = {On-{Device} {Training} {Under} {256KB} {Memory}}, - url = {http://arxiv.org/abs/2206.15472}, - doi = {10.48550/arXiv.2206.15472}, - urldate = {2023-10-31}, - publisher = {arXiv}, - author = {Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, - month = nov, - year = {2022}, - note = {arXiv:2206.15472 [cs]}, - keywords = {Computer Science - Computer Vision and Pattern Recognition}, - annote = {Comment: NeurIPS 2022}, - file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/64BR84L4/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/X65HXDGN/2206.html:text/html}, -} - -@misc{dhar_-device_2020-1, - title = {On-{Device} {Machine} {Learning}: {An} {Algorithms} and {Learning} {Theory} {Perspective}}, - shorttitle = {On-{Device} {Machine} {Learning}}, - url = {http://arxiv.org/abs/1911.00623}, - doi = {10.48550/arXiv.1911.00623}, - urldate = {2023-10-31}, - publisher = {arXiv}, - author = {Dhar, Sauptik and Guo, Junyao and Liu, Jiayi and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak}, - month = jul, - year = {2020}, - note = {arXiv:1911.00623 [cs, stat]}, - keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Statistics - Machine Learning}, - annote = {Comment: Edge Learning, TinyML, Resource Constrained Machine Learning, Deep learning on device, Statistical Learning Theory, 45 pages survey}, - file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/ZKW95X4B/Dhar et al. - 2020 - On-Device Machine Learning An Algorithms and Lear.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/5F6QZ2KV/1911.html:text/html}, -} - -@misc{power_grokking_2022, - title = {Grokking: {Generalization} {Beyond} {Overfitting} on {Small} {Algorithmic} {Datasets}}, - shorttitle = {Grokking}, - url = {http://arxiv.org/abs/2201.02177}, - doi = {10.48550/arXiv.2201.02177}, - urldate = {2023-10-31}, - publisher = {arXiv}, - author = {Power, Alethea and Burda, Yuri and Edwards, Harri and Babuschkin, Igor and Misra, Vedant}, - month = jan, - year = {2022}, - note = {arXiv:2201.02177 [cs]}, - keywords = {Computer Science - Machine Learning}, - annote = {Comment: Correspondence to alethea@openai.com. Code available at: https://github.com/openai/grok}, - file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/9P6A876M/Power et al. - 2022 - Grokking Generalization Beyond Overfitting on Sma.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/VTZ6CQ6J/2201.html:text/html}, -} - -@inproceedings{schwarzschild_just_2021, - title = {Just {How} {Toxic} is {Data} {Poisoning}? {A} {Unified} {Benchmark} for {Backdoor} and {Data} {Poisoning} {Attacks}}, - shorttitle = {Just {How} {Toxic} is {Data} {Poisoning}?}, - url = {https://proceedings.mlr.press/v139/schwarzschild21a.html}, - language = {en}, - urldate = {2023-11-01}, - booktitle = {Proceedings of the 38th {International} {Conference} on {Machine} {Learning}}, - publisher = {PMLR}, - author = {Schwarzschild, Avi and Goldblum, Micah and Gupta, Arjun and Dickerson, John P. and Goldstein, Tom}, - month = jul, - year = {2021}, - note = {ISSN: 2640-3498}, - pages = {9389--9398}, - file = {Full Text PDF:/Users/alex/Zotero/storage/NFGNG6F4/Schwarzschild et al. - 2021 - Just How Toxic is Data Poisoning A Unified Benchm.pdf:application/pdf}, -} - -@inproceedings{abadi_deep_2016, - title = {Deep {Learning} with {Differential} {Privacy}}, - url = {http://arxiv.org/abs/1607.00133}, - doi = {10.1145/2976749.2978318}, - urldate = {2023-11-01}, - booktitle = {Proceedings of the 2016 {ACM} {SIGSAC} {Conference} on {Computer} and {Communications} {Security}}, - author = {Abadi, Martín and Chu, Andy and Goodfellow, Ian and McMahan, H. Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li}, - month = oct, - year = {2016}, - note = {arXiv:1607.00133 [cs, stat]}, - keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning, Statistics - Machine Learning}, - pages = {308--318}, - file = {arXiv Fulltext PDF:/Users/alex/Zotero/storage/EM89UELI/Abadi et al. - 2016 - Deep Learning with Differential Privacy.pdf:application/pdf;arXiv.org Snapshot:/Users/alex/Zotero/storage/VNUTTFRG/1607.html:text/html}, -} - -@inproceedings{kairouz_secure_2015, - title = {Secure {Multi}-party {Differential} {Privacy}}, - volume = {28}, - url = {https://papers.nips.cc/paper_files/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html}, - urldate = {2023-11-01}, - booktitle = {Advances in {Neural} {Information} {Processing} {Systems}}, - publisher = {Curran Associates, Inc.}, - author = {Kairouz, Peter and Oh, Sewoong and Viswanath, Pramod}, - year = {2015}, - file = {Full Text PDF:/Users/alex/Zotero/storage/96IJF4XC/Kairouz et al. - 2015 - Secure Multi-party Differential Privacy.pdf:application/pdf}, -} From b095e72867c6e646ae133c84626ae7ca49e0bae6 Mon Sep 17 00:00:00 2001 From: alxrod Date: Wed, 1 Nov 2023 21:54:09 -0400 Subject: [PATCH 07/59] added federated learnign citations --- references.bib | 58 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git a/references.bib b/references.bib index ec35ec3a..5fe6ccb8 100644 --- a/references.bib +++ b/references.bib @@ -966,3 +966,61 @@ @misc{lin_-device_2022-1 annote = {Comment: NeurIPS 2022}, file = {Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:/Users/alex/Zotero/storage/DNIY32R2/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf}, } + +@inproceedings{mcmahan2017communication, + title={Communication-efficient learning of deep networks from decentralized data}, + author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera}, + booktitle={Artificial intelligence and statistics}, + pages={1273--1282}, + year={2017}, + organization={PMLR} +} +@inproceedings{yang2023online, + title={Online Model Compression for Federated Learning with Large Models}, + author={Yang, Tien-Ju and Xiao, Yonghui and Motta, Giovanni and Beaufays, Fran{\c{c}}oise and Mathews, Rajiv and Chen, Mingqing}, + booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, + pages={1--5}, + year={2023}, + organization={IEEE} +} +@inproceedings{shi2022data, + title={Data selection for efficient model update in federated learning}, + author={Shi, Hongrui and Radu, Valentin}, + booktitle={Proceedings of the 2nd European Workshop on Machine Learning and Systems}, + pages={72--78}, + year={2022} +} +@article{moshawrab2023reviewing, + title={Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives}, + author={Moshawrab, Mohammad and Adda, Mehdi and Bouzouane, Abdenour and Ibrahim, Hussein and Raad, Ali}, + journal={Electronics}, + volume={12}, + number={10}, + pages={2287}, + year={2023}, + publisher={MDPI} +} +@article{zhao2018federated, + title={Federated learning with non-iid data}, + author={Zhao, Yue and Li, Meng and Lai, Liangzhen and Suda, Naveen and Civin, Damon and Chandra, Vikas}, + journal={arXiv preprint arXiv:1806.00582}, + year={2018} +} +@article{smestad2023systematic, + title={A Systematic Literature Review on Client Selection in Federated Learning}, + author={Smestad, Carl and Li, Jingyue}, + journal={arXiv preprint arXiv:2306.04862}, + year={2023} +} +@article{xu2023federated, + title={Federated Learning of Gboard Language Models with Differential Privacy}, + author={Xu, Zheng and Zhang, Yanxiang and Andrew, Galen and Choquette-Choo, Christopher A and Kairouz, Peter and McMahan, H Brendan and Rosenstock, Jesse and Zhang, Yuanbo}, + journal={arXiv preprint arXiv:2305.18465}, + year={2023} +} +@article{xu2023federated, + title={Federated Learning of Gboard Language Models with Differential Privacy}, + author={Xu, Zheng and Zhang, Yanxiang and Andrew, Galen and Choquette-Choo, Christopher A and Kairouz, Peter and McMahan, H Brendan and Rosenstock, Jesse and Zhang, Yuanbo}, + journal={arXiv preprint arXiv:2305.18465}, + year={2023} +} From 56a0327bb5972f4a4bd4a1dd1b34493ce7e4689f Mon Sep 17 00:00:00 2001 From: Michael Schnebly Date: Wed, 1 Nov 2023 18:54:42 -0700 Subject: [PATCH 08/59] References Added: Transfer Learning & Frameworks --- references.bib | 84 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) diff --git a/references.bib b/references.bib index ec35ec3a..33037a0c 100644 --- a/references.bib +++ b/references.bib @@ -1,3 +1,87 @@ +@article{esteva2017dermatologist, +title={Dermatologist-level classification of skin cancer with deep neural networks}, +author={Esteva, Andre and Kuprel, Brett and Novoa, Roberto A and Ko, Justin and Swetter, Susan M and Blau, Helen M and Thrun, Sebastian}, +journal={nature}, +volume={542}, +number={7639}, +pages={115--118}, +year={2017}, +publisher={Nature Publishing Group} +} + +@article{zhuang2020comprehensive, + title={A comprehensive survey on transfer learning}, + author={Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing}, + journal={Proceedings of the IEEE}, + volume={109}, + number={1}, + pages={43--76}, + year={2020}, + publisher={IEEE} +} + +@article{pan2009survey, + title={A survey on transfer learning}, + author={Pan, Sinno Jialin and Yang, Qiang}, + journal={IEEE Transactions on knowledge and data engineering}, + volume={22}, + number={10}, + pages={1345--1359}, + year={2009}, + publisher={IEEE} +} + +@article{david2021tensorflow, + title={Tensorflow lite micro: Embedded machine learning for tinyml systems}, + author={David, Robert and Duke, Jared and Jain, Advait and Janapa Reddi, Vijay and Jeffries, Nat and Li, Jian and Kreeger, Nick and Nappier, Ian and Natraj, Meghna and Wang, Tiezhen and others}, + journal={Proceedings of Machine Learning and Systems}, + volume={3}, + pages={800--811}, + year={2021} +} + +@inproceedings{chen2018tvm, + title={$\{$TVM$\}$: An automated $\{$End-to-End$\}$ optimizing compiler for deep learning}, + author={Chen, Tianqi and Moreau, Thierry and Jiang, Ziheng and Zheng, Lianmin and Yan, Eddie and Shen, Haichen and Cowan, Meghan and Wang, Leyuan and Hu, Yuwei and Ceze, Luis and others}, + booktitle={13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)}, + pages={578--594}, + year={2018} +} + +@article{lin2020mcunet, + title={Mcunet: Tiny deep learning on iot devices}, + author={Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Gan, Chuang and Han, Song and others}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={11711--11722}, + year={2020} +} + +@article{lin2022device, + title={On-device training under 256kb memory}, + author={Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, + journal={Advances in Neural Information Processing Systems}, + volume={35}, + pages={22941--22954}, + year={2022} +} + +@article{cai2020tinytl, + title={Tinytl: Reduce memory, not parameters for efficient on-device learning}, + author={Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={11285--11297}, + year={2020} +} + +@article{kwon2023tinytrain, + title={TinyTrain: Deep Neural Network Training at the Extreme Edge}, + author={Kwon, Young D and Li, Rui and Venieris, Stylianos I and Chauhan, Jagmohan and Lane, Nicholas D and Mascolo, Cecilia}, + journal={arXiv preprint arXiv:2307.09988}, + year={2023} +} + @misc{Thefutur92:online, author = {ARM.com}, title = {The future is being built on Arm: Market diversification continues to drive strong royalty and licensing growth as ecosystem reaches quarter of a trillion chips milestone – Arm®}, From 746f006c5a831edb40bb94f7107bcf1b12818fad Mon Sep 17 00:00:00 2001 From: Jared Ni Date: Wed, 1 Nov 2023 22:07:11 -0400 Subject: [PATCH 09/59] advantages and limitations, on-device adaptations references.bib --- references.bib | 80 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 80 insertions(+) diff --git a/references.bib b/references.bib index ec35ec3a..a555d792 100644 --- a/references.bib +++ b/references.bib @@ -966,3 +966,83 @@ @misc{lin_-device_2022-1 annote = {Comment: NeurIPS 2022}, file = {Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:/Users/alex/Zotero/storage/DNIY32R2/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf}, } + +@article{preparednesspublic, + title={Public Health Law}, + author={Preparedness, Emergency} +} + +@article{wu2022sustainable, + title={Sustainable ai: Environmental implications, challenges and opportunities}, + author={Wu, Carole-Jean and Raghavendra, Ramya and Gupta, Udit and Acun, Bilge and Ardalani, Newsha and Maeng, Kiwan and Chang, Gloria and Aga, Fiona and Huang, Jinshi and Bai, Charles and others}, + journal={Proceedings of Machine Learning and Systems}, + volume={4}, + pages={795--813}, + year={2022} +} + + +@article{lin2022device, + title={On-device training under 256kb memory}, + author={Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, + journal={Advances in Neural Information Processing Systems}, + volume={35}, + pages={22941--22954}, + year={2022} +} + + +@article{dhar2021survey, + title={A survey of on-device machine learning: An algorithms and learning theory perspective}, + author={Dhar, Sauptik and Guo, Junyao and Liu, Jiayi and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak}, + journal={ACM Transactions on Internet of Things}, + volume={2}, + number={3}, + pages={1--49}, + year={2021}, + publisher={ACM New York, NY, USA} +} + +@article{dhar2021survey, + title={A survey of on-device machine learning: An algorithms and learning theory perspective}, + author={Dhar, Sauptik and Guo, Junyao and Liu, Jiayi and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak}, + journal={ACM Transactions on Internet of Things}, + volume={2}, + number={3}, + pages={1--49}, + year={2021}, + publisher={ACM New York, NY, USA} +} + +@article{chen2016training, + title={Training deep nets with sublinear memory cost}, + author={Chen, Tianqi and Xu, Bing and Zhang, Chiyuan and Guestrin, Carlos}, + journal={arXiv preprint arXiv:1604.06174}, + year={2016} +} + +@article{gruslys2016memory, + title={Memory-efficient backpropagation through time}, + author={Gruslys, Audrunas and Munos, R{\'e}mi and Danihelka, Ivo and Lanctot, Marc and Graves, Alex}, + journal={Advances in neural information processing systems}, + volume={29}, + year={2016} +} + + +@inproceedings{inproceedings, +author = {Rouhani, Bita and Mirhoseini, Azalia and Koushanfar, Farinaz}, +year = {2017}, +month = {05}, +pages = {1-4}, +title = {TinyDL: Just-in-time deep learning solution for constrained embedded systems}, +doi = {10.1109/ISCAS.2017.8050343} +} + +@article{li2016lightrnn, + title={LightRNN: Memory and computation-efficient recurrent neural networks}, + author={Li, Xiang and Qin, Tao and Yang, Jian and Liu, Tie-Yan}, + journal={Advances in Neural Information Processing Systems}, + volume={29}, + year={2016} +} From a5e1c5d4f6287ec029ff555333f5b48ec803052a Mon Sep 17 00:00:00 2001 From: AditiR_42 Date: Wed, 1 Nov 2023 22:24:28 -0400 Subject: [PATCH 10/59] add references --- ondevice_learning.qmd | 14 ++--- references.bib | 139 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 146 insertions(+), 7 deletions(-) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index 4986f2fa..35e90157 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -447,9 +447,9 @@ With data poisoning, attackers purchase domains and upload content that influenc This incident is a clear example of data poisoning because malicious actors intentionally manipulated the data used to train and inform the chatbot's responses. The data poisoning resulted in the chatbot adopting harmful biases and producing output that was not intended by its developers. It demonstrates how even small amounts of maliciously crafted data can have a significant impact on the behavior of machine learning models, and highlights the importance of implementing robust data filtering and validation mechanisms to prevent such incidents from occurring. -Real-world impacts of such biases could be dangerous. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like [Five Safes](https://www.researchgate.net/publication/292975549_Five_Safes_designing_data_access_for_research) ensures models are trained on high-quality, representative data. +The real-world impacts of such biases could be dangerous. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like [Five Safes](https://www.researchgate.net/publication/292975549_Five_Safes_designing_data_access_for_research) ensures models are trained on high-quality, representative data. -Data poisoning is a pressing concern for secure on-device learning, since data at the end point cannot be easily monitored in real-time and if models are allowed to adapt on their own then we run the risk of the device acting malicously. But continued research in adversarial machine learning aims to develop robust solutions to detect and mitigate such data attacks. +Data poisoning is a pressing concern for secure on-device learning, since data at the endpoint cannot be easily monitored in real-time and if models are allowed to adapt on their own then we run the risk of the device acting malicously. But continued research in adversarial machine learning aims to develop robust solutions to detect and mitigate such data attacks. ### Adversarial Attacks @@ -464,7 +464,7 @@ To mitigate these risks, several defenses can be employed: * **Data Validation and Sanitization:** Before incorporating new data into the training dataset, it should be thoroughly validated and sanitized to ensure it is not malicious. * **Adversarial Training:** The model can be trained on adversarial examples to make it more robust to these types of attacks. * **Input Validation:** During inference, inputs should be validated to ensure they have not been manipulated to create adversarial examples. -* **Regular Auditing and Monitoring:** Regularly auditing and monitoring the model's behavior can help to detect and mitigate adversarial attacks. In the context of tiny ML systems, this is easier said than done, because it is often hard to monitor embedded machine learning systems at the end point due to communication bandwidth limitations and so forth, which we will discuss in the MLOps chapter. +* **Regular Auditing and Monitoring:** Regularly auditing and monitoring the model's behavior can help to detect and mitigate adversarial attacks. In the context of tiny ML systems, this is easier said than done, because it is often hard to monitor embedded machine learning systems at the endpoint due to communication bandwidth limitations and so forth, which we will discuss in the MLOps chapter. By understanding the potential risks and implementing these defenses, we can help to secure on-device training at the endpoint/edge and mitigate the impact of adversarial attacks. Most people easily confuse data poisoning and adversarial attacks. So here is a table comparing data poisoning and adversarial attacks: @@ -555,7 +555,7 @@ To reduce this memory overhead, TinyTL freezes the majority of the weights so th ![An overview of the TinyTL workflow](images/ondevice_transfer_tinytl.png) -Freezing weights is applicable not just to fully-connected layers but also convolutional and normalization layers. However, only adapting the biases limits the model's ability to learn and adapt to new data. +Freezing weights is applicable not just to fully-connected layers but also to convolutional and normalization layers. However, only adapting the biases limits the model's ability to learn and adapt to new data. To increase adaptability without much additional memory, TinyTL uses a small residual learning model. This refines the intermediate feature maps to produce better outputs, even with fixed weights. The residual model introduces minimal overhead - less than 3.8% on top of the base model. @@ -590,12 +590,12 @@ Here is a table summarizing the key similarities and differences between the Tin The concept of on-device learning is increasingly important for increasing the usability and scalability of TinyML. This chapter explored the intricacies of on-device learning, exploring its advantages and limitations, adaptation strategies, key related algorithms and techniques, security implications, and existing and emerging on-device training frameworks. -On-device learning is, undoubtedly, a groundbreaking paradigm that brings forth numerous advantages for embedded and edge ML deployments. By performing training directly on the endpoint devices, on-device learning obviates the need for continuous cloud connectivity, making it particularly well-suited for IoT and edge computing applications. It comes with benefits such as improved privacy, ease of compliance, resource efficiency. At the same time, on-device learning faces limitations related to hardware constraints, limited data size, and reduced model accuracy and generalization. +On-device learning is, undoubtedly, a groundbreaking paradigm that brings forth numerous advantages for embedded and edge ML deployments. By performing training directly on the endpoint devices, on-device learning obviates the need for continuous cloud connectivity, making it particularly well-suited for IoT and edge computing applications. It comes with benefits such as improved privacy, ease of compliance, and resource efficiency. At the same time, on-device learning faces limitations related to hardware constraints, limited data size, and reduced model accuracy and generalization. Mechanisms such as reduced model complexity, optimization and data compression techniques, and related learning methods such as transfer learning and federated learning allow models to adapt to learn and evolve under resource constraints, thus serving as the bedrock for effective machine learning on edge devices. The critical security concerns in on-device learning highlighted in this chapter, ranging from data poisoning and adversarial attacks to specific risks introduced by on-device learning, must be addressed in real workloads for on-device learning to be a viable paradigm. Effective mitigation strategies, such as data validation, encryption, differential privacy, anomaly detection, and input data validation, are crucial to safeguard on-device learning systems from these threats. -The emergence of specialized on-device training frameworks like Tiny Training Engine, Tiny Transfer Learning, and Tiny Train presents the practical tools to enable efficient on-device training. These frameworks employ various techniques to optimize memory usage, reduce computational overhead, and streamline the on-device training process. +The emergence of specialized on-device training frameworks like Tiny Training Engine, Tiny Transfer Learning, and Tiny Train presents practical tools to enable efficient on-device training. These frameworks employ various techniques to optimize memory usage, reduce computational overhead, and streamline the on-device training process. -In conclusion, on-device learning stands at the forefront of TinyML, promising a future where models can autonomously acquire knowledge and adapt to changing environments on edge devices. The application of on-device learning have the potential to revolutionize various domains, including healthcare, industrial IoT, and smart cities. However, the transformative potential of on-device learning must be balanced with robust security measures to protect against data breaches and adversarial threats. Embracing innovative on-device training frameworks and implementing stringent security protocols are key steps in unlocking the full potential of on-device learning. As this technology continues to evolve, it holds the promise of making our devices smarter, more responsive, and better integrated into our daily lives. +In conclusion, on-device learning stands at the forefront of TinyML, promising a future where models can autonomously acquire knowledge and adapt to changing environments on edge devices. The application of on-device learning has the potential to revolutionize various domains, including healthcare, industrial IoT, and smart cities. However, the transformative potential of on-device learning must be balanced with robust security measures to protect against data breaches and adversarial threats. Embracing innovative on-device training frameworks and implementing stringent security protocols are key steps in unlocking the full potential of on-device learning. As this technology continues to evolve, it holds the promise of making our devices smarter, more responsive, and better integrated into our daily lives. diff --git a/references.bib b/references.bib index ec35ec3a..a80375b1 100644 --- a/references.bib +++ b/references.bib @@ -966,3 +966,142 @@ @misc{lin_-device_2022-1 annote = {Comment: NeurIPS 2022}, file = {Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:/Users/alex/Zotero/storage/DNIY32R2/Lin et al. - 2022 - On-Device Training Under 256KB Memory.pdf:application/pdf}, } + +@inproceedings{schwarzschild2021just, + title={Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks}, + author={Schwarzschild, Avi and Goldblum, Micah and Gupta, Arjun and Dickerson, John P and Goldstein, Tom}, + booktitle={International Conference on Machine Learning}, + pages={9389--9398}, + year={2021}, + organization={PMLR} +} + +@article{biggio2014pattern, + title={Pattern recognition systems under attack: Design issues and research challenges}, + author={Biggio, Battista and Fumera, Giorgio and Roli, Fabio}, + journal={International Journal of Pattern Recognition and Artificial Intelligence}, + volume={28}, + number={07}, + pages={1460002}, + year={2014}, + publisher={World Scientific} +} + +@article{desai2020five, + title={Five Safes: designing data access for research; 2016}, + author={Desai, Tanvi and Ritchie, Felix and Welpton, Richard}, + journal={URL https://www2. uwe. ac. uk/faculties/bbs/Documents/1601. pdf}, + year={2020} +} + +@inproceedings{hong2023publishing, + title={Publishing Efficient On-device Models Increases Adversarial Vulnerability}, + author={Hong, Sanghyun and Carlini, Nicholas and Kurakin, Alexey}, + booktitle={2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)}, + pages={271--290}, + year={2023}, + organization={IEEE} +} + +@article{kairouz2015secure, + title={Secure multi-party differential privacy}, + author={Kairouz, Peter and Oh, Sewoong and Viswanath, Pramod}, + journal={Advances in neural information processing systems}, + volume={28}, + year={2015} +} + +@article{shamir1979share, + title={How to share a secret}, + author={Shamir, Adi}, + journal={Communications of the ACM}, + volume={22}, + number={11}, + pages={612--613}, + year={1979}, + publisher={ACm New York, NY, USA} +} + +@article{dwork2014algorithmic, + title={The algorithmic foundations of differential privacy}, + author={Dwork, Cynthia and Roth, Aaron and others}, + journal={Foundations and Trends{\textregistered} in Theoretical Computer Science}, + volume={9}, + number={3--4}, + pages={211--407}, + year={2014}, + publisher={Now Publishers, Inc.} +} + +@inproceedings{abadi2016deep, + title={Deep learning with differential privacy}, + author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li}, + booktitle={Proceedings of the 2016 ACM SIGSAC conference on computer and communications security}, + pages={308--318}, + year={2016} +} + +@article{koshti2011cumulative, + title={Cumulative sum control chart}, + author={Koshti, VV}, + journal={International journal of physics and mathematical sciences}, + volume={1}, + number={1}, + pages={28--32}, + year={2011} +} + +@article{chen2023learning, + title={Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning}, + author={Chen, Zhiyong and Xu, Shugong}, + journal={EURASIP Journal on Audio, Speech, and Music Processing}, + volume={2023}, + number={1}, + pages={33}, + year={2023}, + publisher={Springer} +} + +@article{david2021tensorflow, + title={Tensorflow lite micro: Embedded machine learning for tinyml systems}, + author={David, Robert and Duke, Jared and Jain, Advait and Janapa Reddi, Vijay and Jeffries, Nat and Li, Jian and Kreeger, Nick and Nappier, Ian and Natraj, Meghna and Wang, Tiezhen and others}, + journal={Proceedings of Machine Learning and Systems}, + volume={3}, + pages={800--811}, + year={2021} +} + +@inproceedings{chen2018tvm, + title={$\{$TVM$\}$: An automated $\{$End-to-End$\}$ optimizing compiler for deep learning}, + author={Chen, Tianqi and Moreau, Thierry and Jiang, Ziheng and Zheng, Lianmin and Yan, Eddie and Shen, Haichen and Cowan, Meghan and Wang, Leyuan and Hu, Yuwei and Ceze, Luis and others}, + booktitle={13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)}, + pages={578--594}, + year={2018} +} + +@article{lin2020mcunet, + title={Mcunet: Tiny deep learning on iot devices}, + author={Lin, Ji and Chen, Wei-Ming and Lin, Yujun and Gan, Chuang and Han, Song and others}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={11711--11722}, + year={2020} +} + +@article{lin2022device, + title={On-device training under 256kb memory}, + author={Lin, Ji and Zhu, Ligeng and Chen, Wei-Ming and Wang, Wei-Chen and Gan, Chuang and Han, Song}, + journal={Advances in Neural Information Processing Systems}, + volume={35}, + pages={22941--22954}, + year={2022} +} + +@article{cai2020tinytl, + title={Tinytl: Reduce memory, not parameters for efficient on-device learning}, + author={Cai, Han and Gan, Chuang and Zhu, Ligeng and Han, Song}, + journal={Advances in Neural Information Processing Systems}, + volume={33}, + pages={11285--11297}, + year={2020} +} From 865816f41071a55caef696ccaa21ce13a670d04a Mon Sep 17 00:00:00 2001 From: Michael Schnebly Date: Wed, 1 Nov 2023 19:27:40 -0700 Subject: [PATCH 11/59] Add citation links to Transfer Learning section --- ondevice_learning.qmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index 4986f2fa..0c61136a 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -220,7 +220,7 @@ Transfer learning plays an important role in making on-device AI practical by al Transfer learning has revolutionized the way models are developed and deployed, both in the cloud and at the edge. Transfer learning is being used in the real world. One such example is the use of transfer learning to develop AI models that can detect and diagnose diseases from medical images, such as X-rays, MRI scans, and CT scans. For example, researchers at Stanford University have developed a [transfer learning model that can detect cancer in lung X-rays](https://pubmed.ncbi.nlm.nih.gov/28117445/) with an accuracy of 97%. It use in production scenarios can be broadly categorized into two stages: pre-deployment and post-deployment. -![Transfering knowledge between tasks](images/ondevice_transfer_learning_apps.png) +![Transfering knowledge between tasks. ([Credit](https://ieeexplore.ieee.org/document/9134370))](images/ondevice_transfer_learning_apps.png) ### Pre-Deployment Specialization @@ -235,7 +235,7 @@ Here's how it works in practice: * **Validation:** After fine-tuning, the model is validated to ensure it meets the performance criteria for the specialized task. * **Deployment:** Once validated, the specialized model is then deployed into the production environment. -This method significantly reduces the time and computational resources required to train a model from scratch. By adopting transfer learning, embedded systems can achieve high accuracy on specialized tasks without the need to gather extensive data or expend significant computational resources on training from the ground up. +This [method](https://ieeexplore.ieee.org/document/5288526) significantly reduces the time and computational resources required to train a model from scratch. By adopting transfer learning, embedded systems can achieve high accuracy on specialized tasks without the need to gather extensive data or expend significant computational resources on training from the ground up. ### Post-Deployment Adaptation From 8acbe9b7aa457c8c42e0edb38d273d97b7b7bae8 Mon Sep 17 00:00:00 2001 From: alxrod Date: Wed, 1 Nov 2023 23:22:56 -0400 Subject: [PATCH 12/59] fixed syntax issues w references.bib file --- references.bib | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/references.bib b/references.bib index e95ba6fb..de0e1490 100644 --- a/references.bib +++ b/references.bib @@ -1129,7 +1129,8 @@ @article{li2016lightrnn journal={Advances in Neural Information Processing Systems}, volume={29}, year={2016} -======= +} + @inproceedings{schwarzschild2021just, title={Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks}, author={Schwarzschild, Avi and Goldblum, Micah and Gupta, Arjun and Dickerson, John P and Goldstein, Tom}, @@ -1267,6 +1268,7 @@ @article{cai2020tinytl volume={33}, pages={11285--11297}, year={2020} +} @inproceedings{mcmahan2017communication, title={Communication-efficient learning of deep networks from decentralized data}, From 568a8db579554ce2e11aff609dabbaa32ffd0490 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Thu, 2 Nov 2023 12:29:55 -0400 Subject: [PATCH 13/59] Reference updates and fixes --- ondevice_learning.qmd | 100 ++++++++++++++++++++++++------------------ references.bib | 42 +++++++++--------- 2 files changed, 79 insertions(+), 63 deletions(-) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index 3be6da98..237895cb 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -53,15 +53,15 @@ One major advantage of on-device learning is the significant reduction in bandwi This reduction in data transmission not only saves bandwidth but also translates to lower costs for servers, networking, and data storage in the cloud. Large organizations, which might spend millions on cloud infrastructure to train models on-device data, can experience dramatic cost reductions through on-device learning. In the era of Generative AI, where costs have been escalating significantly, finding ways to keep expenses down has become increasingly important. -Furthermore, the energy and environmental costs associated with running large server farms are also diminished. Data centers are known to consume vast amounts of energy, contributing to greenhouse gas emissions. By reducing the need for extensive cloud-based infrastructure, on-device learning plays a part in mitigating the [environmental impact of data processing](http://arxiv.org/abs/2111.00364#). +Furthermore, the energy and environmental costs associated with running large server farms are also diminished. Data centers are known to consume vast amounts of energy, contributing to greenhouse gas emissions. By reducing the need for extensive cloud-based infrastructure, on-device learning plays a part in mitigating the environmental impact of data processing [@wu2022sustainable]. -Specifically for endpoint applications, on-device learning minimizes the number of network API calls needed to run inference through a cloud provider. For applications with millions of users, the cumulative costs associated with bandwidth and API calls can quickly escalate. In contrast, performing training and inferences locally is considerably more efficient and cost-effective. [On-device learning](http://arxiv.org/abs/2111.00364#) has been shown to reduce training memory requirements, drastically improve memory efficiency, and reduce up to 20% in per-iteration latency under the state-of-the-art optimizations. +Specifically for endpoint applications, on-device learning minimizes the number of network API calls needed to run inference through a cloud provider. For applications with millions of users, the cumulative costs associated with bandwidth and API calls can quickly escalate. In contrast, performing training and inferences locally is considerably more efficient and cost-effective. On-device learning has been shown to reduce training memory requirements, drastically improve memory efficiency, and reduce up to 20% in per-iteration latency under the state-of-the-art optimizations [@dhar2021survey]. Another key benefit of on-device learning is the potential for Internet of Things devices to continuously adapt their machine-learning model to new data for continuous, lifelong learning. On-device models can quickly become outdated as user behavior, data patterns, and preferences change. Continuous learning enables the model to efficiently adapt to new data and improvements and maintain high model performance over time. ### Limitations -While traditional cloud-based machine learning systems have access to nearly endless computing resources, on-device learning is often restricted by the limitations in computational and storage power of the edge device that the model is trained on. By definition, an [edge device](http://arxiv.org/abs/1911.00623) is a device with restrained computing, memory, and energy resources, that cannot be easily increased or decreased. Thus, the reliance on edge devices can restrict the complexity, efficiency, and size of on-device machine learning models. +While traditional cloud-based machine learning systems have access to nearly endless computing resources, on-device learning is often restricted by the limitations in computational and storage power of the edge device that the model is trained on. By definition, an edge device is a device with restrained computing, memory, and energy resources, that cannot be easily increased or decreased. Thus, the reliance on edge devices can restrict the complexity, efficiency, and size of on-device machine learning models. #### Compute resources @@ -107,7 +107,7 @@ The limited data and optimizations required for on-device learning can negativel - Noisy user-generated data reduces quality. Sensor noise or improper data labeling by non-experts may degrade training. - Optimizations like pruning and quantization trade off accuracy for efficiency. An 8-bit quantized model runs faster but less accurately than a 32-bit model. -So while cloud models achieve high accuracy with massive datasets and no constraints, on-device models can struggle to generalize. [Some studies](http://arxiv.org/abs/2206.15472) show that on-device training matches cloud accuracy on select tasks. However, performance on real-world workloads requires further study. +So while cloud models achieve high accuracy with massive datasets and no constraints, on-device models can struggle to generalize. Some studies show that on-device training matches cloud accuracy on select tasks. However, performance on real-world workloads requires further study [@lin2022device]. For instance, a cloud model can accurately detect pneumonia in chest X-rays from thousands of hospitals. However, an on-device model trained only on a small local patient population may fail to generalize. @@ -122,42 +122,45 @@ Accuracy, generalization, and speed challenges pose hurdles to adopting on-devic ## Ondevice Adaptation -In a machine learning task, resource consumption [mainly](http://arxiv.org/abs/1911.00623) comes from three sources: +In a machine learning task, resource consumption mainly comes from three sources: * The ML model itself; * The optimization process during model learning * Storing and processing the dataset used for learning. -Correspondingly, there are [three](http://arxiv.org/abs/1911.00623) approaches to adapting existing ML algorithms onto resource-constrained devices: +Correspondingly, there are three approaches to adapting existing ML algorithms onto resource-constrained devices: * Reducing the complexity of the ML model * Modifying optimizations to reduce training resource requirements * Creating new storage-efficient data representations -In the following section, we will review these on-device learning adaptation methods. More details on model optimizations can be found in the [Model Optimizations](https://harvard-edge.github.io/cs249r_book/optimizations.html) chapter. +In the following section, we will review these on-device learning adaptation methods. More details on model optimizations can be found in the [Model Optimizations](./optimizations.qmd) chapter. ### Reducing Model Complexity + In this section, we will briefly discuss ways to reduce model complexity to adapt ML models on-device. For details of reducing model complexity, please refer to the Model Optimization Chapter. #### Traditional ML Algorithms -Due to the compute and memory limitations of edge devices, select traditional ML algorithms are great candidates for on-device learning applications due to their [lightweight nature](http://arxiv.org/abs/1911.00623). Some example algorithms with low resource footprints include Naive Bayes Classifier, Support Vector Machines (SVMs), Linear Regression, Logistic Regression, and select Decision Tree algorithms. + +Due to the compute and memory limitations of edge devices, select traditional ML algorithms are great candidates for on-device learning applications due to their lightweight nature. Some example algorithms with low resource footprints include Naive Bayes Classifier, Support Vector Machines (SVMs), Linear Regression, Logistic Regression, and select Decision Tree algorithms. With some refinements, these classical ML algorithms can be adapted to specific hardware architectures and perform simple tasks, and their low performance requirements make it easy to integrate continuous learning even on edge devices. #### Pruning + Pruning is a technique used to reduce the size and complexity of a decision tree or other types of machine learning models, like neural networks, to improve their efficiency and generalization performance. This is beneficial for training models on edge devices, where we want to minimize the resource usage while maintaining competitive accuracy. The primary goal of pruning is to remove parts of the model that do not contribute significantly to its predictive power while retaining the most informative aspects. In the context of decision trees, pruning involves removing some of the branches (subtrees) from the tree, leading to a smaller and simpler tree. In the context of DNN, pruning is used to reduce the number of neurons (units) or connections in the network. ![The network complexity reduces drasticaly after prunning. ([Credit](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*rw2zAHw9Xlm7nSq1PCKbzQ.png))](images/ondevice_pruning.png) - #### Reducing Complexity of Deep Learning Models -Traditional cloud-based DNN frameworks have too much memory overhead to be used on-device. [For example](http://arxiv.org/abs/2206.15472), deep learning systems like PyTorch and TensorFlow require hundreds of megabytes of memory overhead when training models such as MobilenetV2-w0.35, and the overhead scales as the number of training parameters increases. -Current research for lightweight DNNs mostly explore [CNN architectures](http://arxiv.org/abs/1911.00623). Several bare-metal frameworks designed for running Neural Network on MCUs by keeping computational overhead and memory footprint low also exist. Some examples include MNN, TVM, and TensorFlow Lite. However, they can only perform inference during forward pass and lack support for [back-propagation](http://arxiv.org/abs/2206.15472). While these models are designed for edge deployment, their reduction in model weights and architectural connections led to reduced resource requirements for [continuous learning](http://arxiv.org/abs/1911.00623). +Traditional cloud-based DNN frameworks have too much memory overhead to be used on-device. For example, deep learning systems like PyTorch and TensorFlow require hundreds of megabytes of memory overhead when training models such as MobilenetV2-w0.35, and the overhead scales as the number of training parameters increases. -The tradeoff between performance and model support is clear when adapting the most popular DNN systems. How do we adapt existing DNN models to resource-constrained settings while maintaining support for back-propagation and continuous learning? Latest research suggests algorithm and system codesign techniques that help reduce the resource consumption of machine learning training on edge devices. Utilizing techniques such as quantization-aware scaling (QAS), sparse updates, and other cutting edge techniques, on-device learning is possible on embedded systems with a few hundred kilobytes of RAM without additional memory while maintaining [high accuracy](http://arxiv.org/abs/2206.15472). +Current research for lightweight DNNs mostly explore CNN architectures. Several bare-metal frameworks designed for running Neural Network on MCUs by keeping computational overhead and memory footprint low also exist. Some examples include MNN, TVM, and TensorFlow Lite. However, they can only perform inference during forward pass and lack support for back-propagation. While these models are designed for edge deployment, their reduction in model weights and architectural connections led to reduced resource requirements for continuous learning. + +The tradeoff between performance and model support is clear when adapting the most popular DNN systems. How do we adapt existing DNN models to resource-constrained settings while maintaining support for back-propagation and continuous learning? Latest research suggests algorithm and system codesign techniques that help reduce the resource consumption of machine learning training on edge devices. Utilizing techniques such as quantization-aware scaling (QAS), sparse updates, and other cutting edge techniques, on-device learning is possible on embedded systems with a few hundred kilobytes of RAM without additional memory while maintaining high accuracy [@lin2022device]. ### Modifying Optimization Processes @@ -165,7 +168,7 @@ Choosing the right optimization strategy is important for DNN training on-device #### Quantization-Aware Scaling -Quantization is a common method for reducing the memory footprint of DNN training. Although this could introduce new errors, these errors can be mitigated by designing a model to characterize this statistical error. For example, models could use stochastic rounding or introduce the quantization error into the [gradient updates](http://arxiv.org/abs/1911.00623). +Quantization is a common method for reducing the memory footprint of DNN training. Although this could introduce new errors, these errors can be mitigated by designing a model to characterize this statistical error. For example, models could use stochastic rounding or introduce the quantization error into the gradient updates. A specific algorithmic technique is Quantization-Aware Scaling (QAS), used to improve the performance of neural networks on low-precision hardware, such as edge devices and mobile devices or TinyML systems, by adjusting the scale factors during the quantization process. @@ -181,21 +184,21 @@ The QAS process involves two main steps: * **Quantization and scaling:** After training, the model is quantized to low-precision format, and the scale factors are adjusted to minimize the quantization errors. The scale factors are chosen based on the distribution of the weights and activations in the model, and are adjusted to ensure that the quantized values are within the range of the low-precision format. -Quantization-aware scaling (QAS) is used to overcome the difficulties of optimizing models on tiny devices. Without needing hyperparamter tuning. QAS automatically scales tensor gradients with various bit-precisions. This in turn stabilizes the training process and matches the accuracy of [floating-point precision](https://arxiv.org/abs/2206.15472). +QAS is used to overcome the difficulties of optimizing models on tiny devices. Without needing hyperparamter tuning. QAS automatically scales tensor gradients with various bit-precisions. This in turn stabilizes the training process and matches the accuracy of floating-point precision. #### Sparse Updates -Although QAS enables optimizing a quantized model, it uses a large amount of memory that is unrealistic for on-device training. So spare update is used to reduce the memory footprint of full backward computation. Instead of pruning weights for inference, sparse update prunes the gradient during backwards propagation to update the model sparsely. In other words, [sparse update](https://arxiv.org/abs/2206.15472) skips computing gradients of less important layers and sub-tensors. -However, determining the optimal sparse update scheme given a constraining memory budget can be challenging due to the large search space. For example, the MCUNet model has 43 convolutional layers and a search space of approximately 1030. One technique to address this issue is contribution analysis. Contribution analysis measures the accuracy improvement from biases (updating the last few biases compared to only updating the classifier) and weights (updating the weight of one extra layer compared to only having a bias update). By trying to maximize these improvements, contribution analysis automatically derives an optimal sparse update scheme for enabling on-device training. +Although QAS enables optimizing a quantized model, it uses a large amount of memory that is unrealistic for on-device training. So spare update is used to reduce the memory footprint of full backward computation. Instead of pruning weights for inference, sparse update prunes the gradient during backwards propagation to update the model sparsely. In other words, sparse update skips computing gradients of less important layers and sub-tensors. +However, determining the optimal sparse update scheme given a constraining memory budget can be challenging due to the large search space. For example, the MCUNet model has 43 convolutional layers and a search space of approximately 1030. One technique to address this issue is contribution analysis. Contribution analysis measures the accuracy improvement from biases (updating the last few biases compared to only updating the classifier) and weights (updating the weight of one extra layer compared to only having a bias update). By trying to maximize these improvements, contribution analysis automatically derives an optimal sparse update scheme for enabling on-device training. #### Layer-Wise Training -Other methods besides quantization can help optimize routines. One such method is layer-wise training. A significant memory consumer of DNN training is the end-to-end back-propagation. This requires all intermediate feature maps to be stored so the model can calculate gradients. An alternative to this approach that reduces the memory footprint of DNN training is [sequential layer-by-layer training](https://arxiv.org/abs/1604.06174). Instead of training end-to-end, training a single layer at a time helps avoid having to store intermediate feature maps. +Other methods besides quantization can help optimize routines. One such method is layer-wise training. A significant memory consumer of DNN training is the end-to-end back-propagation. This requires all intermediate feature maps to be stored so the model can calculate gradients. An alternative to this approach that reduces the memory footprint of DNN training is sequential layer-by-layer training [@chen2016training]. Instead of training end-to-end, training a single layer at a time helps avoid having to store intermediate feature maps. #### Trading Computation for Memory -The strategy of trading computation for memory involves releasing some of the memory being used to store intermediate results. Instead, these results can be recomputed as needed. [Reducing memory](https://arxiv.org/abs/1606.03401) in exchange for more computation is shown to reduce the memory footprint of DNN training to fit into almost any budget while also minimizing computational cost. +The strategy of trading computation for memory involves releasing some of the memory being used to store intermediate results. Instead, these results can be recomputed as needed. Reducing memory in exchange for more computation is shown to reduce the memory footprint of DNN training to fit into almost any budget while also minimizing computational cost [@gruslys2016memory]. ### Developing New Data Representations @@ -203,9 +206,9 @@ The dimensionality and volume of the training data can significantly impact on-d #### Data Compression -The goal of data compression is to reach high accuracies while limiting the amount of training data. One method to achieve this is prioritizing [sample complexity](https://arxiv.org/abs/1911.00623): the amount of training data required for the algorithm to reach a target accuracy. +The goal of data compression is to reach high accuracies while limiting the amount of training data. One method to achieve this is prioritizing sample complexity: the amount of training data required for the algorithm to reach a target accuracy [@dhar2021survey]. -Other more common methods of data compression focus on reducing the dimensionality and the volume of the training data. For example, an approach could take advantage of matrix sparsity to reduce the memory footprint of storing training data. Training data can be transformed into a lower-dimensional embedding and factorized into a dictionary matrix multiplied by a [block-sparse coefficient matrix](https://www.researchgate.net/publication/320089058_TinyDL_Just-in-time_deep_learning_solution_for_constrained_embedded_systems). Another example could involve representing words from a large language training dataset in a more [compressed vector format](https://arxiv.org/abs/1610.09893). +Other more common methods of data compression focus on reducing the dimensionality and the volume of the training data. For example, an approach could take advantage of matrix sparsity to reduce the memory footprint of storing training data. Training data can be transformed into a lower-dimensional embedding and factorized into a dictionary matrix multiplied by a block-sparse coefficient matrix [@rouhani2017tinydl]. Another example could involve representing words from a large language training dataset in a more compressed vector format [@li2016lightrnn]. ## Transfer Learning @@ -218,7 +221,7 @@ If we want to customize the model for the ondevice characteristics, training a n Transfer learning plays an important role in making on-device AI practical by allowing us to leverage pre-trained models and fine-tune them for specific tasks, thereby reducing the computational resources and time required for training. The combination of on-device AI and transfer learning opens up new possibilities for AI applications that are more privacy-conscious and responsive to user needs. -Transfer learning has revolutionized the way models are developed and deployed, both in the cloud and at the edge. Transfer learning is being used in the real world. One such example is the use of transfer learning to develop AI models that can detect and diagnose diseases from medical images, such as X-rays, MRI scans, and CT scans. For example, researchers at Stanford University have developed a [transfer learning model that can detect cancer in lung X-rays](https://pubmed.ncbi.nlm.nih.gov/28117445/) with an accuracy of 97%. It use in production scenarios can be broadly categorized into two stages: pre-deployment and post-deployment. +Transfer learning has revolutionized the way models are developed and deployed, both in the cloud and at the edge. Transfer learning is being used in the real world. One such example is the use of transfer learning to develop AI models that can detect and diagnose diseases from medical images, such as X-rays, MRI scans, and CT scans. For example, researchers at Stanford University have developed a transfer learning model that can detect cancer in lung X-rays with an accuracy of 97% [@esteva2017dermatologist]. It use in production scenarios can be broadly categorized into two stages: pre-deployment and post-deployment. ![Transfering knowledge between tasks. ([Credit](https://ieeexplore.ieee.org/document/9134370))](images/ondevice_transfer_learning_apps.png) @@ -235,7 +238,7 @@ Here's how it works in practice: * **Validation:** After fine-tuning, the model is validated to ensure it meets the performance criteria for the specialized task. * **Deployment:** Once validated, the specialized model is then deployed into the production environment. -This [method](https://ieeexplore.ieee.org/document/5288526) significantly reduces the time and computational resources required to train a model from scratch. By adopting transfer learning, embedded systems can achieve high accuracy on specialized tasks without the need to gather extensive data or expend significant computational resources on training from the ground up. +This method significantly reduces the time and computational resources required to train a model from scratch [@pan2009survey]. By adopting transfer learning, embedded systems can achieve high accuracy on specialized tasks without the need to gather extensive data or expend significant computational resources on training from the ground up. ### Post-Deployment Adaptation @@ -368,23 +371,29 @@ With this proposed structure, there are a few key vectors for optimizing federat ### Communication Efficiency -One of the key bottlenecks in federated learning is communication. Every time a client trains the model, they must communicate back to the server their updates. Similarly, once the server has averaged all the updates, it must send them back to the client. On large networks of millions of devices, this incurs huge bandwidth and resource cost. As the field of federated learning advances, a few optimizations have been developed to minimize this communication. To address the footprint of the model, researchers have developed model compression techniques. In the client server protocol, federated learning can also minimize communication through selective sharing of updates on clients. Finally, efficient aggregation techniques can also streamline the communication process +One of the key bottlenecks in federated learning is communication. Every time a client trains the model, they must communicate back to the server their updates. Similarly, once the server has averaged all the updates, it must send them back to the client. On large networks of millions of devices, this incurs huge bandwidth and resource cost. As the field of federated learning advances, a few optimizations have been developed to minimize this communication. To address the footprint of the model, researchers have developed model compression techniques. In the client server protocol, federated learning can also minimize communication through selective sharing of updates on clients. Finally, efficient aggregation techniques can also streamline the communication process. ### Model Compression -In standard federated learning, the server must communicate the entire model to each client and then the client must send back all of the updated weights. This means that the easiest way to reduce both the memory and communication footprint on the client is to minimize the size of the model needed to be communicated. To do this, we can employ all of the previously discussed model optimization strategies. In 2022, another team at Google proposed [Online Model Compression (OMC)](https://doi.org/10.48550/arXiv.2205.03494) wherein each client communicates via a compressed format and decompresses the model on the fly for training, allocating and deallocating the full memory for the model only for a short period while training. The model is compressed through a range of various quantization strategies elaborated upon in their paper. Meanwhile the server can update the uncompressed model, decompressing and applying updates as they come in. +In standard federated learning, the server must communicate the entire model to each client and then the client must send back all of the updated weights. This means that the easiest way to reduce both the memory and communication footprint on the client is to minimize the size of the model needed to be communicated. To do this, we can employ all of the previously discussed model optimization strategies. + +In 2022, another team at Google proposed wherein each client communicates via a compressed format and decompresses the model on the fly for training [@yang2023online], allocating and deallocating the full memory for the model only for a short period while training. The model is compressed through a range of various quantization strategies elaborated upon in their paper. Meanwhile the server can update the uncompressed model, decompressing and applying updates as they come in. ### Selective Update Sharing -There are a breadth of methods for selectively sharing updates. The general principle is that reducing the portion of the model that the clients are training on the edge reduces the memory necessary for training and the size of communication to the server. In basic federated learning, the client trains the entire model. This means that when a client sends an update to the server it has gradients for every weight in the network. However, you cannot just reduce communication by sending pieces of those gradients to the server from each client because the gradients are part of an entire update required to improve the model. Instead, you need to architecturally design the model such that the clients each train only a small portion of the broader model, reducing the total communication while still gaining the benefit of training on client data. A paper from the University of Sheffield applies this concept to a CNN by [splitting the global model into two parts](https://doi.org/10.1145/3517207.3526980): an upper and lower part. +There are a breadth of methods for selectively sharing updates. The general principle is that reducing the portion of the model that the clients are training on the edge reduces the memory necessary for training and the size of communication to the server. In basic federated learning, the client trains the entire model. This means that when a client sends an update to the server it has gradients for every weight in the network. + +However, we cannot just reduce communication by sending pieces of those gradients to the server from each client because the gradients are part of an entire update required to improve the model. Instead, you need to architecturally design the model such that the clients each train only a small portion of the broader model, reducing the total communication while still gaining the benefit of training on client data. A paper from the University of Sheffield applies this concept to a CNN by splitting the global model into two parts: an upper and lower part as shown below [@shi2022data]. + ![Split model architecture for selective sharing proposed by [University of Sheffield](https://doi.org/10.1145/3517207.3526980)](images/ondevice_split_model.png) + The lower part is designed to focus on generic features in the dataset while the upper part trained on those generic features is designed to be more sensitive to the activation maps. This means that the lower part of the model is trained through standard federated averaging across all of the clients. Meanwhile, the upper part of the model is trained entirely on the server side from the activation maps generated by the clients. This approach drastically reduces communication for the model while still making the network robust to various types of input found in the data on the client devices. ### Optimized Aggregation In addition to reducing the communication overhead, optimizing the aggregation function can improve model training speed and accuracy in certain federated learning use cases. While the standard for aggregation is just averaging, there are various other approaches which can improve model efficiency, accuracy, and security. One alternative is clipped averaging which clips the model updates within a specific range. Another strategy to preserve security is differential privacy average aggregation. This approach integrates differential privacy into the aggregations tep to protect client identities. Each client adds a layer of random noise to their updates before communicating to the server. The server then updates the server with the noisy updates, meaning that the amount of noise needs to be tuned carefully to balance privacy and accuracy. -In addition to security enhancing aggregation methods, there are several modifications to the aggregation methods that can improve training speed and performance by adding client metadata along with the weight updates. Momentum aggregation is a technique which helps address the convergence problem. In federated learning, clients data can be extremely heterogeneous depending on the different environments devices are in. That means that many models with heterogeneous data may struggle to converge. Each client stores a momentum term locally which tracks the pace of change over several updates. With clients communicating this momentum, the server can factor in the rate of change of each update when changing the global model to accelerate convergence. Similarly, weighted aggregation can factoro in the client performance or other parameters like device type or network connection strength to adjust the weight with which the server should incorporate the model updates. ([Further description of specific aggregation algorithms](https://doi.org/10.3390/electronics12102287)) +In addition to security enhancing aggregation methods, there are several modifications to the aggregation methods that can improve training speed and performance by adding client metadata along with the weight updates. Momentum aggregation is a technique which helps address the convergence problem. In federated learning, clients data can be extremely heterogeneous depending on the different environments devices are in. That means that many models with heterogeneous data may struggle to converge. Each client stores a momentum term locally which tracks the pace of change over several updates. With clients communicating this momentum, the server can factor in the rate of change of each update when changing the global model to accelerate convergence. Similarly, weighted aggregation can factoro in the client performance or other parameters like device type or network connection strength to adjust the weight with which the server should incorporate the model updates. Further description of specific aggregation algorithms are described by @moshawrab2023reviewing. ### Handling non-IID Data @@ -396,7 +405,7 @@ When using federated learning to train a model across many client devices, it is - If you have wildlife sensors in remote areas, connectivity may not be equally distributed causing some clients in certain regions to be able to send more model updates than others. If those regions have different wildlife activity from certain species, that could skew the updates toward those animals -There are a few approaches to addressing non-IID data in federated learning. One approach would be to change the aggregation algorithm. If you use a weighted aggregation algorithm, you can adjust based on different client properties like region, sensor properties, or connectivity ([Further algorithm description here](https://doi.org/10.48550/arXiv.1806.00582)) +There are a few approaches to addressing non-IID data in federated learning. One approach would be to change the aggregation algorithm. If you use a weighted aggregation algorithm, you can adjust based on different client properties like region, sensor properties, or connectivity [@zhao2018federated]. ### Client Selection @@ -406,7 +415,9 @@ When selecting clients, there are three main components to consider: data hetero ### An Example of Deployed Federated Learning: G board -A primary example of a deployed federated learning system is Google’s Keyboard, Gboard, for android devices. In their implementation of federated learning for the keyboard, Google focused on employing differential privacy techniques to protect the user’s data and identity. [Gboard](http://arxiv.org/abs/2305.18465) leverages language models for several key features such as Next Word Prediction (NWP), Smart Compose (SC), and On-The-Fly rescoring (OTF). NWP will anticipate the next word the user is trying to type based on the previous one. SC gives inline suggestions to speed up the typing based on each character. OTF will re-rank the proposed next words based on the active typing process. All three of these models need to run quickly on the edge and federated learning can accelerate training on the users data. However, uploading every word a user typed to the cloud for training would be a massive privacy violation. Therefore, federated learning with an emphasis on differential privacy protects the user while still enabling a better user experience. +A primary example of a deployed federated learning system is Google’s Keyboard, Gboard, for android devices. In their implementation of federated learning for the keyboard, Google focused on employing differential privacy techniques to protect the user’s data and identity. Gboard leverages language models for several key features such as Next Word Prediction (NWP), Smart Compose (SC), and On-The-Fly rescoring (OTF) [@xu2023federated]. + +NWP will anticipate the next word the user is trying to type based on the previous one. SC gives inline suggestions to speed up the typing based on each character. OTF will re-rank the proposed next words based on the active typing process. All three of these models need to run quickly on the edge and federated learning can accelerate training on the users data. However, uploading every word a user typed to the cloud for training would be a massive privacy violation. Therefore, federated learning with an emphasis on differential privacy protects the user while still enabling a better user experience. ![Examples of Google G Board Features](images/ondevice_gboard_example.png) @@ -416,7 +427,7 @@ To accomplish this goal, Google employed their algorithm DP-FTRL which provides ### Benchmarking for Federated Learning: MedPerf -One of the richest examples of data on the edge is medical devices. These devices store some of the most personal data on users but offer huge advances in personalized treatment and better accuracy in medical AI. Given these two factors, medical devices are the perfect use case for federated learning. [MedPerf](https://doi.org/10.1038/s42256-023-00652-2) is an open source platform used to benchmark models using federated evaluation. Instead of just training models via federated learning, MedPerf takes the model to edge devices to test it against personalized data while preserving privacy. In this way a benchmark committee can evaluate various models in the real world on edge devices while still preserving patient anonymity. +One of the richest examples of data on the edge is medical devices. These devices store some of the most personal data on users but offer huge advances in personalized treatment and better accuracy in medical AI. Given these two factors, medical devices are the perfect use case for federated learning. [MedPerf](https://doi.org/10.1038/s42256-023-00652-2) is an open source platform used to benchmark models using federated evaluation [@karargyris2023federated]. Instead of just training models via federated learning, MedPerf takes the model to edge devices to test it against personalized data while preserving privacy. In this way a benchmark committee can evaluate various models in the real world on edge devices while still preserving patient anonymity. ## Security Concerns @@ -431,6 +442,7 @@ Performing machine learning model training and adaptation on end-user devices al Any system that performs learning on-device introduces security concerns, as it may expose vulnerabilities in larger scale models. There are numerous security risks associated with any machine learning model, but these risks have specific consequences for on-device learning. Fortunately, there are methods to mitigate these risks to improve the real-world performance of on-device learning. ### Data Poisoning + On-device machine learning introduces unique data security challenges compared to traditional cloud-based training. In particular, data poisoning attacks pose a serious threat during on-device learning. Adversaries can manipulate training data to degrade model performance when deployed. Several data poisoning attack techniques exist: @@ -439,7 +451,7 @@ Several data poisoning attack techniques exist: * **Data Insertion:** It introduces fake or distorted inputs into the training set. This could include pixelated images, noisy audio, or garbled text. * **Logic Corruption:** This alters the underlying [patterns](https://www.worldscientific.com/doi/10.1142/S0218001414600027) in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more [afraid of data poisoning](https://proceedings.mlr.press/v139/schwarzschild21a.html) than other adversarial machine learning concerns. -What makes data poisoning alarming is how it exploits the discrepancy between curated datasets and live training data. Consider a cat photo dataset collected from the internet. In the weeks later when this data trains a model on-device, new cat photos on the web differ significantly. +What makes data poisoning alarming is how it exploits the discrepancy between curated datasets and live training data. Consider a cat photo dataset collected from the internet. In the weeks later when this data trains a model on-device, new cat photos on the web differ significantly. With data poisoning, attackers purchase domains and upload content that influences a portion of the training data. Even small data changes significantly impact the model's learned behavior. Consequently, poisoning can instill racist, sexist, or other harmful biases if unchecked. @@ -447,7 +459,7 @@ With data poisoning, attackers purchase domains and upload content that influenc This incident is a clear example of data poisoning because malicious actors intentionally manipulated the data used to train and inform the chatbot's responses. The data poisoning resulted in the chatbot adopting harmful biases and producing output that was not intended by its developers. It demonstrates how even small amounts of maliciously crafted data can have a significant impact on the behavior of machine learning models, and highlights the importance of implementing robust data filtering and validation mechanisms to prevent such incidents from occurring. -The real-world impacts of such biases could be dangerous. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like [Five Safes](https://www.researchgate.net/publication/292975549_Five_Safes_designing_data_access_for_research) ensures models are trained on high-quality, representative data. +The real-world impacts of such biases could be dangerous. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like Five Safes ensures models are trained on high-quality, representative data [@desai2016five]. Data poisoning is a pressing concern for secure on-device learning, since data at the endpoint cannot be easily monitored in real-time and if models are allowed to adapt on their own then we run the risk of the device acting malicously. But continued research in adversarial machine learning aims to develop robust solutions to detect and mitigate such data attacks. @@ -481,13 +493,14 @@ By understanding the potential risks and implementing these defenses, we can hel ### On-Device Learning Security Concerns -While data poisoning and adversarial attacks are common concerns for machine learning models in general, on-device learning introduces unique security risks. When on-device variants of large-scale models are published, adversaries can exploit these smaller models to attack their larger counterparts. Research has demonstrated that as on-device models and full-scale models become more similar, the vulnerability of the original large-scale models increases significantly. For instance, evaluations across 19 Deep Neural Networks (DNNs) revealed that exploiting on-device models could increase the vulnerability of the original large-scale models by [up to 100 times](http://arxiv.org/abs/2212.13700). +While data poisoning and adversarial attacks are common concerns for machine learning models in general, on-device learning introduces unique security risks. When on-device variants of large-scale models are published, adversaries can exploit these smaller models to attack their larger counterparts. Research has demonstrated that as on-device models and full-scale models become more similar, the vulnerability of the original large-scale models increases significantly. For instance, evaluations across 19 DNNs revealed that exploiting on-device models could increase the vulnerability of the original large-scale models by up to 100 times [@hong2023publishing]. There are three primary types of security risks specific to on-device learning: +- **Transfer-Based Attacks**: These attacks exploit the transferability property between a surrogate model (an approximation of the target model, similar to an on-device model) and a remote target model (the original full-scale model). Attackers generate adversarial examples using the surrogate model, which can then be used to deceive the target model. For example, imagine an on-device model designed to identify spam emails. An attacker could use this model to generate a spam email that is not detected by the larger, full-scale email filtering system. + +- **Optimization-Based Attacks**: These attacks generate adversarial examples for transfer-based attacks using some form of objective function, and iteratively modify inputs to achieve the desired outcome. Gradient estimation attacks, for example, approximate the model’s gradient using query outputs (such as softmax confidence scores), while gradient-free attacks use the model’s final decision (the predicted class) to approximate the gradient, albeit requiring many more queries. -- **Transfer-Based Attacks**: These attacks exploit the transferability property between a surrogate model (an approximation of the target model, similar to an on-device model) and a remote target model (the original full-scale model). Attackers generate adversarial examples using the surrogate model, which can then be used to deceive the [target model](http://arxiv.org/abs/2212.13700). For example, imagine an on-device model designed to identify spam emails. An attacker could use this model to generate a spam email that is not detected by the larger, full-scale email filtering system. -- **Optimization-Based Attacks**: These attacks generate adversarial examples for transfer-based attacks using some form of objective function, and iteratively modify inputs to achieve the desired outcome. Gradient estimation attacks, for example, approximate the model’s gradient using query outputs (such as softmax confidence scores), while gradient-free attacks use the model’s final decision (the predicted class) to approximate the gradient, albeit requiring many more [queries](http://arxiv.org/abs/2212.13700). - **Query Attacks with Transfer Priors**: These attacks combine elements of transfer-based and optimization-based attacks. They reverse engineer on-device models to serve as surrogates for the target full-scale model. In other words, attackers use the smaller on-device model to understand how the larger model works, and then use this knowledge to attack the full-scale model. By understanding these specific risks associated with on-device learning, we can develop more robust security protocols to protect both on-device and full-scale models from potential attacks. @@ -496,11 +509,11 @@ By understanding these specific risks associated with on-device learning, we can To mitigate the numerous security risks associated with on-device learning, a variety of methods can be employed. These methods may be specific to the type of attack or serve as a general tool to bolster security. -One strategy to reduce security risks is to diminish the similarity between on-device models and full-scale models, thereby reducing transferability by up to 90%. This method, known as similarity-unpairing, addresses the problem that arises when adversaries exploit the input-gradient similarity between the two models. By fine-tuning the full-scale model to create a new version with similar accuracy but different input gradients, we can then construct the on-device model by quantizing this updated full-scale model. This unpairing reduces the vulnerability of on-device models by limiting the exposure of the original full-scale model. Importantly, the [order of finetuning and quantization](http://arxiv.org/abs/2212.13700) can be varied while still achieving risk mitigation. +One strategy to reduce security risks is to diminish the similarity between on-device models and full-scale models, thereby reducing transferability by up to 90%. This method, known as similarity-unpairing, addresses the problem that arises when adversaries exploit the input-gradient similarity between the two models. By fine-tuning the full-scale model to create a new version with similar accuracy but different input gradients, we can then construct the on-device model by quantizing this updated full-scale model. This unpairing reduces the vulnerability of on-device models by limiting the exposure of the original full-scale model. Importantly, the order of finetuning and quantization can be varied while still achieving risk mitigation [@hong2023publishing]. To tackle data poisoning, it is imperative to source datasets from trusted and reliable [vendors](https://www.eetimes.com/cybersecurity-threats-loom-over-endpoint-ai-systems/?_gl=1%2A17zgs0d%2A_ga%2AMTY0MzA1MTAyNS4xNjk4MDgyNzc1%2A_ga_ZLV02RYCZ8%2AMTY5ODA4Mjc3NS4xLjAuMTY5ODA4Mjc3NS42MC4wLjA). -In combating adversarial attacks, several strategies can be employed. A proactive approach involves generating adversarial examples and incorporating them into the model's training dataset, thereby fortifying the model against such attacks. Tools like [CleverHans](http://github.com/cleverhans-lab/cleverhans), an open-source training library, are instrumental in creating adversarial examples. [Defense distillation](http://arxiv.org/abs/2212.13700) is another effective strategy, wherein the on-device model outputs probabilities of different classifications rather than definitive decisions, making it more challenging for adversarial examples to exploit the model. +In combating adversarial attacks, several strategies can be employed. A proactive approach involves generating adversarial examples and incorporating them into the model's training dataset, thereby fortifying the model against such attacks. Tools like [CleverHans](http://github.com/cleverhans-lab/cleverhans), an open-source training library, are instrumental in creating adversarial examples. Defense distillation is another effective strategy, wherein the on-device model outputs probabilities of different classifications rather than definitive decisions [@hong2023publishing], making it more challenging for adversarial examples to exploit the model. The theft of intellectual property is another significant concern when deploying on-device models. Intellectual property theft is a concern when deploying on-device models, as adversaries may attempt to reverse-engineer the model to steal the underlying technology. To safeguard against intellectual property theft, the binary executable of the trained model should be stored on a microcontroller unit with encrypted software and secured physical interfaces of the chip. Furthermore, the final dataset used for training the model should be kept [private](https://www.eetimes.com/cybersecurity-threats-loom-over-endpoint-ai-systems/?_gl=1%2A17zgs0d%2A_ga%2AMTY0MzA1MTAyNS4xNjk4MDgyNzc1%2A_ga_ZLV02RYCZ8%2AMTY5ODA4Mjc3NS4xLjAuMTY5ODA4Mjc3NS42MC4wLjA). @@ -515,24 +528,25 @@ There are a variety of different ways to secure on-device training data. Each of Encryption serves as the first line of defense for training data. This involves implementing end-to-end encryption for both local storage on devices and communication channels to prevent unauthorized access to raw training data. Trusted execution environments, such as [Intel SGX](https://www.intel.com/content/www/us/en/architecture-and-technology/software-guard-extensions.html) and [ARM TrustZone](https://www.arm.com/technologies/trustzone-for-cortex-a#:~:text=Arm%20TrustZone%20technology%20offers%20an,trust%20based%20on%20PSA%20guidelines.), are essential for facilitating secure training on encrypted data. -Additionally, when aggregating updates from multiple devices, [secure multi-party computation protocols](https://papers.nips.cc/paper_files/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html) like [Shamir's Secret Sharing](https://web.mit.edu/6.857/OldStuff/Fall03/ref/Shamir-HowToShareASecret.pdf) can be employed to enhance security. A practical application of this is in collaborative on-device learning, where cryptographic privacy-preserving aggregation of user model updates can be implemented. This technique effectively hides individual user data even during the aggregation phase. +Additionally, when aggregating updates from multiple devices, secure multi-party computation protocols can be employed to enhance security [@kairouz2015secure]. A practical application of this is in collaborative on-device learning, where cryptographic privacy-preserving aggregation of user model updates can be implemented. This technique effectively hides individual user data even during the aggregation phase. #### Differential Privacy -Differential privacy is another crucial strategy for protecting training data. By injecting calibrated statistical noise into the data, we can mask individual records while still extracting valuable [population patterns](https://www.cis.upenn.edu/~aaroth/privacybook.html). Managing the [privacy budget](https://arxiv.org/abs/1607.00133) across multiple training iterations and reducing noise as the model converges is also vital. Methods such as formally provable differential privacy, which may include adding Laplace or Gaussian noise scaled to the dataset's sensitivity, can be employed. +Differential privacy is another crucial strategy for protecting training data. By injecting calibrated statistical noise into the data, we can mask individual records while still extracting valuable population patterns [@dwork2014algorithmic]. Managing the privacy budget across multiple training iterations and reducing noise as the model converges is also vital [@abadi2016deep]. Methods such as formally provable differential privacy, which may include adding Laplace or Gaussian noise scaled to the dataset's sensitivity, can be employed. #### Anomaly Detection -Anomaly detection plays a pivotal role in identifying and mitigating potential data poisoning attacks. This can be achieved through statistical analyses like Principal Component Analysis (PCA) and clustering, which help to detect deviations in aggregated training data. [Time-series methods](https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/reference/ReferencesPapers.aspx?ReferenceID=1163726) such as Cumulative Sum (CUSUM) charts are useful for identifying shifts indicative of potential poisoning. Comparing current data distributions with previously seen clean data distributions can also help to flag anomalies. Moreover, suspected poisoned batches should be removed from the training update aggregation process. For example, spot checks on subsets of training images on devices can be conducted using photoDNA hashes to identify poisoned inputs. +Anomaly detection plays a pivotal role in identifying and mitigating potential data poisoning attacks. This can be achieved through statistical analyses like Principal Component Analysis (PCA) and clustering, which help to detect deviations in aggregated training data. Time-series methods such as [Cumulative Sum (CUSUM)](https://en.wikipedia.org/wiki/CUSUM) charts are useful for identifying shifts indicative of potential poisoning. Comparing current data distributions with previously seen clean data distributions can also help to flag anomalies. Moreover, suspected poisoned batches should be removed from the training update aggregation process. For example, spot checks on subsets of training images on devices can be conducted using photoDNA hashes to identify poisoned inputs. #### Input Data Validation -Lastly, input data validation is essential for ensuring the integrity and validity of input data before it is fed into the training model, thereby protecting against adversarial payloads. Similarity measures, such as cosine distance, can be employed to catch inputs that deviate significantly from the expected distribution. Suspicious inputs that may contain adversarial payloads should be quarantined and sanitized. Furthermore, parser access to training data should be restricted to validated code paths only. Leveraging hardware security features, such as ARM Pointer Authentication, can prevent memory corruption (ARM Limited, 2023). An example of this is implementing input integrity checks on audio training data used by smart speakers before processing by the [speech recognition model](https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-023-00299-2). +Lastly, input data validation is essential for ensuring the integrity and validity of input data before it is fed into the training model, thereby protecting against adversarial payloads. Similarity measures, such as cosine distance, can be employed to catch inputs that deviate significantly from the expected distribution. Suspicious inputs that may contain adversarial payloads should be quarantined and sanitized. Furthermore, parser access to training data should be restricted to validated code paths only. Leveraging hardware security features, such as ARM Pointer Authentication, can prevent memory corruption (ARM Limited, 2023). An example of this is implementing input integrity checks on audio training data used by smart speakers before processing by the speech recognition model [@chen2023learning]. ## On-Device Training Frameworks -Embedded inference frameworks like [TF-Lite Micro](https://arxiv.org/abs/2010.08678), [TVM](http://arxiv.org/abs/1802.04799), and [MCUNet](http://arxiv.org/abs/2007.10319) provide a slim runtime for running neural network models on microcontrollers and other resource-constrained devices. However, they don't support on-device training. Training requires its own set of [specialized tools](https://arxiv.org/abs/2206.15472) due to the impact of quantization on gradient calculation and the memory footprint of backpropagation. -In recent years, there are a handful of tools and frameworks that have started to emerge that enable on-device training, and these include [Tiny Training Engine](https://arxiv.org/abs/2206.15472), [TinyTL](https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html), and [TinyTrain](http://arxiv.org/abs/2307.09988). +Embedded inference frameworks like TF-Lite Micro [@david2021tensorflow], TVM [@chen2018tvm], and MCUNet [@lin2020mcunet] provide a slim runtime for running neural network models on microcontrollers and other resource-constrained devices. However, they don't support on-device training. Training requires its own set of specialized tools due to the impact of quantization on gradient calculation and the memory footprint of backpropagation [@lin2022device]. + +In recent years, there are a handful of tools and frameworks that have started to emerge that enable on-device training, and these include Tiny Training Engine [@lin2022device], TinyTL [@cai2020tinytl], and TinyTrain [@kwon2023tinytrain]. ### Tiny Training Engine @@ -549,7 +563,7 @@ Specifically, TTE follows four main steps: ### Tiny Transfer Learning -Tiny Transfer Learning ([TinyTL](https://proceedings.neurips.cc/paper/2020/hash/81f7acabd411274fcf65ce2070ed568a-Abstract.html)) enables memory-efficient on-device training through a technique called weight freezing. During training, much of the memory bottleneck comes from storing intermediate activations and updating the weights in the neural network. +Tiny Transfer Learning (TinyTL) enables memory-efficient on-device training through a technique called weight freezing. During training, much of the memory bottleneck comes from storing intermediate activations and updating the weights in the neural network. To reduce this memory overhead, TinyTL freezes the majority of the weights so they do not need to be updated during training. This eliminates the need to store intermediate activations for frozen parts of the network. TinyTL only fine-tunes the bias terms, which are much smaller than the weights. diff --git a/references.bib b/references.bib index de0e1490..7ccede52 100644 --- a/references.bib +++ b/references.bib @@ -91,6 +91,26 @@ @misc{Thefutur92:online note = {(Accessed on 09/16/2023)} } +@article{desai2016five, + title={Five Safes: designing data access for research}, + author={Desai, Tanvi and Ritchie, Felix and Welpton, Richard and others}, + journal={Economics Working Paper Series}, + volume={1601}, + pages={28}, + year={2016} +} + +@article{karargyris2023federated, + title={Federated benchmarking of medical artificial intelligence with MedPerf}, + author={Karargyris, Alexandros and Umeton, Renato and Sheller, Micah J and Aristizabal, Alejandro and George, Johnu and Wuest, Anna and Pati, Sarthak and Kassem, Hasan and Zenk, Maximilian and Baid, Ujjwal and others}, + journal={Nature Machine Intelligence}, + volume={5}, + number={7}, + pages={799--810}, + year={2023}, + publisher={Nature Publishing Group UK London} +} + @article{ramcharan2017deep, title={Deep learning for image-based cassava disease detection}, author={Ramcharan, Amanda and Baranowski, Kelsee and McCloskey, Peter and Ahmed, Babuali and Legg, James and Hughes, David P}, @@ -1075,18 +1095,6 @@ @article{lin2022device year={2022} } - -@article{dhar2021survey, - title={A survey of on-device machine learning: An algorithms and learning theory perspective}, - author={Dhar, Sauptik and Guo, Junyao and Liu, Jiayi and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak}, - journal={ACM Transactions on Internet of Things}, - volume={2}, - number={3}, - pages={1--49}, - year={2021}, - publisher={ACM New York, NY, USA} -} - @article{dhar2021survey, title={A survey of on-device machine learning: An algorithms and learning theory perspective}, author={Dhar, Sauptik and Guo, Junyao and Liu, Jiayi and Tripathi, Samarth and Kurup, Unmesh and Shah, Mohak}, @@ -1114,7 +1122,7 @@ @article{gruslys2016memory } -@inproceedings{inproceedings, +@inproceedings{rouhani2017tinydl, author = {Rouhani, Bita and Mirhoseini, Azalia and Koushanfar, Farinaz}, year = {2017}, month = {05}, @@ -1320,10 +1328,4 @@ @article{xu2023federated author={Xu, Zheng and Zhang, Yanxiang and Andrew, Galen and Choquette-Choo, Christopher A and Kairouz, Peter and McMahan, H Brendan and Rosenstock, Jesse and Zhang, Yuanbo}, journal={arXiv preprint arXiv:2305.18465}, year={2023} -} -@article{xu2023federated, - title={Federated Learning of Gboard Language Models with Differential Privacy}, - author={Xu, Zheng and Zhang, Yanxiang and Andrew, Galen and Choquette-Choo, Christopher A and Kairouz, Peter and McMahan, H Brendan and Rosenstock, Jesse and Zhang, Yuanbo}, - journal={arXiv preprint arXiv:2305.18465}, - year={2023} -} +} \ No newline at end of file From 5760a5a376809f31008b060d3df99e7fb9e89d52 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Thu, 2 Nov 2023 12:40:05 -0400 Subject: [PATCH 14/59] Added learning objectives --- ondevice_learning.qmd | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/ondevice_learning.qmd b/ondevice_learning.qmd index 237895cb..12ac2e19 100644 --- a/ondevice_learning.qmd +++ b/ondevice_learning.qmd @@ -2,6 +2,21 @@ ![_DALL·E 3 Prompt: Drawing of a smartphone with its internal components exposed, revealing diverse miniature engineers of different genders and skin tones actively working on the machine learning model. The engineers, including men, women, and non-binary individuals, are tuning parameters, repairing connections, and enhancing the network on the fly. Data flows into the machine learning model, being processed in real-time, and generating output inferences._](./images/cover_ondevice_learning.png) +::: {.callout-tip} +## Learning Objectives + +* Understand on-device learning and how it differs from cloud-based training + +* Recognize the benefits and limitations of on-device learning + +* Examine strategies to adapt models through complexity reduction, optimization, and data compression + +* Understand related concepts like federated learning and transfer learning + +* Analyze the security implications of on-device learning and mitigation strategies + +::: + On-device Learning represents a significant innovation for embedded and edge IoT devices, enabling models to train and update directly on small local devices. This contrasts with traditional methods where models are trained on expansive cloud computing resources before deployment. With On-Device Learning, devices like smart speakers, wearables, and industrial sensors can refine models in real-time based on local data, without needing to transmit data externally. For example, a voice-enabled smart speaker could learn and adapt to its owner's speech patterns and vocabulary right on the device. But there is no such thing as free lunch, therefore in this chapter, we will discuss both the benefits and the limitations of on-device learning. ## Introduction From fd5bdcacdf3d82f1665074c6cb51dae2f3acb645 Mon Sep 17 00:00:00 2001 From: happyappledog Date: Mon, 6 Nov 2023 15:24:00 -0500 Subject: [PATCH 15/59] Added a medical example in AI for Good chapter --- ai_for_good.qmd | 7 +++++++ references.bib | 11 +++++++++++ 2 files changed, 18 insertions(+) diff --git a/ai_for_good.qmd b/ai_for_good.qmd index 33b946ac..669c1735 100644 --- a/ai_for_good.qmd +++ b/ai_for_good.qmd @@ -100,6 +100,13 @@ A collaborative research team from the University of Khartoum and the ICTP is ex This portable, self-contained system shows great promise for entomology. The researchers suggest it could revolutionize insect monitoring and vector control strategies in remote areas. By providing cheaper, easier mosquito analytics, TinyML could significantly bolster malaria eradication efforts. Its versatility and minimal power needs make it ideal for field use in isolated, off-grid regions with scarce resources but high disease burden. +### TinyML Design Contest (TDC) - TinyML contest in healthcare +The first TinyML contest in healthcare, TDC’22 [@jia2023life] was held in 2022 to motivate participating teams to design AI/ML algorithms for detecting life-threatening ventricular arrhythmias (VAs) and deploy them on Implantable Cardioverter Defibrillators (ICDs). VAs are the main cause of sudden cardiac death (SCD). People at high risk of SCD rely on the ICD to deliver proper and in-time defibrillation treatment (i.e., shocking the heart back into normal rhythm) when experiencing life-threatening VAs. On-device algorithm for early and in-time life-threatening VA detection will increase the chances of survival. + +The proposed AI/ML algorithm needs to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU: (1) detection performance; (2) inference latency; and (3) memory occupation by the program of AI/ML algorithms. The champion, GaTech EIC Lab, obtained 0.972 in $F_β$ (F1 score with a higher weight to recall), 1.747 ms in latency and 26.39 kB in memory footprint with a deep neural network. + +An ICD with on-device VA detection algorithm was inplanted in a clinical trial (see the author's [presentation](https://youtu.be/vx2gWzAr85A?t=2359)). + ## Science In many scientific fields, researchers are limited by the quality and resolution of data they can collect. They often must infer the true parameters of interest indirectly, using approximate correlations and models built on sparse data points. This constrains the accuracy of scientific understanding and predictions. diff --git a/references.bib b/references.bib index 4501f577..9c36b8db 100644 --- a/references.bib +++ b/references.bib @@ -690,6 +690,17 @@ @inproceedings{altayeb2022classifying year={2022} } +@article{jia2023life, + title={Life-threatening ventricular arrhythmia detection challenge in implantable cardioverter--defibrillators}, + author={Jia, Zhenge and Li, Dawei and Xu, Xiaowei and Li, Na and Hong, Feng and Ping, Lichuan and Shi, Yiyu}, + journal={Nature Machine Intelligence}, + volume={5}, + number={5}, + pages={554--555}, + year={2023}, + publisher={Nature Publishing Group UK London} +} + @article{yamashita2023coffee, title={Coffee disease classification at the edge using deep learning}, author={Yamashita, Jo{\~a}o Vitor Yukio Bordin and Leite, Jo{\~a}o Paulo RR}, From 40c0c2eaaf7cec822e3b7f612e6f789253c87c16 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Tue, 7 Nov 2023 07:48:51 -0500 Subject: [PATCH 16/59] Reworked the flow a bit --- ai_for_good.qmd | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/ai_for_good.qmd b/ai_for_good.qmd index 669c1735..dec411e1 100644 --- a/ai_for_good.qmd +++ b/ai_for_good.qmd @@ -100,12 +100,13 @@ A collaborative research team from the University of Khartoum and the ICTP is ex This portable, self-contained system shows great promise for entomology. The researchers suggest it could revolutionize insect monitoring and vector control strategies in remote areas. By providing cheaper, easier mosquito analytics, TinyML could significantly bolster malaria eradication efforts. Its versatility and minimal power needs make it ideal for field use in isolated, off-grid regions with scarce resources but high disease burden. -### TinyML Design Contest (TDC) - TinyML contest in healthcare -The first TinyML contest in healthcare, TDC’22 [@jia2023life] was held in 2022 to motivate participating teams to design AI/ML algorithms for detecting life-threatening ventricular arrhythmias (VAs) and deploy them on Implantable Cardioverter Defibrillators (ICDs). VAs are the main cause of sudden cardiac death (SCD). People at high risk of SCD rely on the ICD to deliver proper and in-time defibrillation treatment (i.e., shocking the heart back into normal rhythm) when experiencing life-threatening VAs. On-device algorithm for early and in-time life-threatening VA detection will increase the chances of survival. +### TinyML Design Contest in Healthcare -The proposed AI/ML algorithm needs to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU: (1) detection performance; (2) inference latency; and (3) memory occupation by the program of AI/ML algorithms. The champion, GaTech EIC Lab, obtained 0.972 in $F_β$ (F1 score with a higher weight to recall), 1.747 ms in latency and 26.39 kB in memory footprint with a deep neural network. +The first TinyML contest in healthcare, TDC’22 [@jia2023life], was held in 2022 to motivate participating teams to design AI/ML algorithms for detecting life-threatening ventricular arrhythmias (VAs) and deploy them on Implantable Cardioverter Defibrillators (ICDs). VAs are the main cause of sudden cardiac death (SCD). People at high risk of SCD rely on the ICD to deliver proper and timely defibrillation treatment (i.e., shocking the heart back into normal rhythm) when experiencing life-threatening VAs. -An ICD with on-device VA detection algorithm was inplanted in a clinical trial (see the author's [presentation](https://youtu.be/vx2gWzAr85A?t=2359)). +An on-device algorithm for early and timely life-threatening VA detection will increase the chances of survival. The proposed AI/ML algorithm needed to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU for (1) detection performance; (2) inference latency; and (3) memory occupation by the program of AI/ML algorithms. + +The champion, GaTech EIC Lab, obtained 0.972 in $F_\beta$ (F1 score with a higher weight to recall), 1.747 ms in latency and 26.39 kB in memory footprint with a deep neural network. An ICD with an on-device VA detection algorithm was [implanted in a clinical trial](https://youtu.be/vx2gWzAr85A?t=2359). ## Science From 6a434b4b5c05b114e635fdd3cb82014b399b7e6e Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 7 Nov 2023 12:52:00 +0000 Subject: [PATCH 17/59] Update readme and contributors.qmd with contributors --- .all-contributorsrc | 141 +++++++++++++++++++++++--------------------- README.md | 31 +++++----- contributors.qmd | 31 +++++----- 3 files changed, 107 insertions(+), 96 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index bcd2d6ed..1dd4e3b4 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -7,10 +7,10 @@ ], "contributors": [ { - "login": "mpstewart1", - "name": "Matthew Stewart", - "avatar_url": "https://avatars.githubusercontent.com/mpstewart1", - "profile": "https://github.com/mpstewart1", + "login": "BaeHenryS", + "name": "Henry Bae", + "avatar_url": "https://avatars.githubusercontent.com/BaeHenryS", + "profile": "https://github.com/BaeHenryS", "contributions": [ "doc" ] @@ -25,37 +25,19 @@ ] }, { - "login": "DivyaAmirtharaj", - "name": "Divya", - "avatar_url": "https://avatars.githubusercontent.com/DivyaAmirtharaj", - "profile": "https://github.com/DivyaAmirtharaj", - "contributions": [ - "doc" - ] - }, - { - "login": "jessicaquaye", - "name": "Jessica Quaye", - "avatar_url": "https://avatars.githubusercontent.com/jessicaquaye", - "profile": "https://github.com/jessicaquaye", - "contributions": [ - "doc" - ] - }, - { - "login": "colbybanbury", - "name": "Colby Banbury", - "avatar_url": "https://avatars.githubusercontent.com/colbybanbury", - "profile": "https://github.com/colbybanbury", + "login": "sophiacho1", + "name": "sophiacho1", + "avatar_url": "https://avatars.githubusercontent.com/sophiacho1", + "profile": "https://github.com/sophiacho1", "contributions": [ "doc" ] }, { - "login": "profvjreddi", - "name": "Vijay Janapa Reddi", - "avatar_url": "https://avatars.githubusercontent.com/profvjreddi", - "profile": "https://github.com/profvjreddi", + "login": "Naeemkh", + "name": "naeemkh", + "avatar_url": "https://avatars.githubusercontent.com/Naeemkh", + "profile": "https://github.com/Naeemkh", "contributions": [ "doc" ] @@ -70,10 +52,10 @@ ] }, { - "login": "uchendui", - "name": "Ikechukwu Uchendu", - "avatar_url": "https://avatars.githubusercontent.com/uchendui", - "profile": "https://github.com/uchendui", + "login": "aptl26", + "name": "aptl26", + "avatar_url": "https://avatars.githubusercontent.com/aptl26", + "profile": "https://github.com/aptl26", "contributions": [ "doc" ] @@ -88,46 +70,46 @@ ] }, { - "login": "BaeHenryS", - "name": "Henry Bae", - "avatar_url": "https://avatars.githubusercontent.com/BaeHenryS", - "profile": "https://github.com/BaeHenryS", + "login": "marcozennaro", + "name": "Marco Zennaro", + "avatar_url": "https://avatars.githubusercontent.com/marcozennaro", + "profile": "https://github.com/marcozennaro", "contributions": [ "doc" ] }, { - "login": "mmaz", - "name": "Mark Mazumder", - "avatar_url": "https://avatars.githubusercontent.com/mmaz", - "profile": "https://github.com/mmaz", + "login": "jaysonzlin", + "name": "Jayson Lin", + "avatar_url": "https://avatars.githubusercontent.com/jaysonzlin", + "profile": "https://github.com/jaysonzlin", "contributions": [ "doc" ] }, { - "login": "Naeemkh", - "name": "naeemkh", - "avatar_url": "https://avatars.githubusercontent.com/Naeemkh", - "profile": "https://github.com/Naeemkh", + "login": "happyappledog", + "name": "happyappledog", + "avatar_url": "https://avatars.githubusercontent.com/happyappledog", + "profile": "https://github.com/happyappledog", "contributions": [ "doc" ] }, { - "login": "ishapira1", - "name": "ishapira", - "avatar_url": "https://avatars.githubusercontent.com/ishapira1", - "profile": "https://github.com/ishapira1", + "login": "profvjreddi", + "name": "Vijay Janapa Reddi", + "avatar_url": "https://avatars.githubusercontent.com/profvjreddi", + "profile": "https://github.com/profvjreddi", "contributions": [ "doc" ] }, { - "login": "sophiacho1", - "name": "sophiacho1", - "avatar_url": "https://avatars.githubusercontent.com/sophiacho1", - "profile": "https://github.com/sophiacho1", + "login": "mmaz", + "name": "Mark Mazumder", + "avatar_url": "https://avatars.githubusercontent.com/mmaz", + "profile": "https://github.com/mmaz", "contributions": [ "doc" ] @@ -142,10 +124,10 @@ ] }, { - "login": "marcozennaro", - "name": "Marco Zennaro", - "avatar_url": "https://avatars.githubusercontent.com/marcozennaro", - "profile": "https://github.com/marcozennaro", + "login": "jessicaquaye", + "name": "Jessica Quaye", + "avatar_url": "https://avatars.githubusercontent.com/jessicaquaye", + "profile": "https://github.com/jessicaquaye", "contributions": [ "doc" ] @@ -160,19 +142,46 @@ ] }, { - "login": "aptl26", - "name": "aptl26", - "avatar_url": "https://avatars.githubusercontent.com/aptl26", - "profile": "https://github.com/aptl26", + "login": "uchendui", + "name": "Ikechukwu Uchendu", + "avatar_url": "https://avatars.githubusercontent.com/uchendui", + "profile": "https://github.com/uchendui", "contributions": [ "doc" ] }, { - "login": "jaysonzlin", - "name": "Jayson Lin", - "avatar_url": "https://avatars.githubusercontent.com/jaysonzlin", - "profile": "https://github.com/jaysonzlin", + "login": "ishapira1", + "name": "ishapira", + "avatar_url": "https://avatars.githubusercontent.com/ishapira1", + "profile": "https://github.com/ishapira1", + "contributions": [ + "doc" + ] + }, + { + "login": "DivyaAmirtharaj", + "name": "Divya", + "avatar_url": "https://avatars.githubusercontent.com/DivyaAmirtharaj", + "profile": "https://github.com/DivyaAmirtharaj", + "contributions": [ + "doc" + ] + }, + { + "login": "mpstewart1", + "name": "Matthew Stewart", + "avatar_url": "https://avatars.githubusercontent.com/mpstewart1", + "profile": "https://github.com/mpstewart1", + "contributions": [ + "doc" + ] + }, + { + "login": "colbybanbury", + "name": "Colby Banbury", + "avatar_url": "https://avatars.githubusercontent.com/colbybanbury", + "profile": "https://github.com/colbybanbury", "contributions": [ "doc" ] diff --git a/README.md b/README.md index d671a594..a6b4deda 100644 --- a/README.md +++ b/README.md @@ -88,31 +88,32 @@ quarto render - + - - - + + + - - - + - + + + + - - - + + - - - - + + + + +
    Matthew Stewart
    Matthew Stewart

    📖
    Henry Bae
    Henry Bae

    📖
    oishib
    oishib

    📖
    Divya
    Divya

    📖
    Jessica Quaye
    Jessica Quaye

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    Colby Banbury
    Colby Banbury

    📖
    sophiacho1
    sophiacho1

    📖
    naeemkh
    naeemkh

    📖
    Shvetank Prakash
    Shvetank Prakash

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    Vijay Janapa Reddi
    Vijay Janapa Reddi

    📖
    Shvetank Prakash
    Shvetank Prakash

    📖
    Ikechukwu Uchendu
    Ikechukwu Uchendu

    📖
    aptl26
    aptl26

    📖
    Marcelo Rovai
    Marcelo Rovai

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    Henry Bae
    Henry Bae

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    Marco Zennaro
    Marco Zennaro

    📖
    Jayson Lin
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    happyappledog
    happyappledog

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    Vijay Janapa Reddi
    Vijay Janapa Reddi

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    Mark Mazumder
    Mark Mazumder

    📖
    naeemkh
    naeemkh

    📖
    ishapira
    ishapira

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    sophiacho1
    sophiacho1

    📖
    Jeffrey Ma
    Jeffrey Ma

    📖
    Jessica Quaye
    Jessica Quaye

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    sjohri20
    sjohri20

    📖
    Marco Zennaro
    Marco Zennaro

    📖
    sjohri20
    sjohri20

    📖
    aptl26
    aptl26

    📖
    Jayson Lin
    Jayson Lin

    📖
    Ikechukwu Uchendu
    Ikechukwu Uchendu

    📖
    ishapira
    ishapira

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    Divya
    Divya

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    Matthew Stewart
    Matthew Stewart

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    Colby Banbury
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    diff --git a/contributors.qmd b/contributors.qmd index cd599f9e..c2f56231 100644 --- a/contributors.qmd +++ b/contributors.qmd @@ -8,31 +8,32 @@ We extend our sincere thanks to the diverse group of individuals who have genero - + - - - + + + - - - + - + + + + - - - + + - - - - + + + + +
    Matthew Stewart
    Matthew Stewart

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    Henry Bae
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    oishib
    oishib

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    Divya
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    Jessica Quaye
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    Colby Banbury
    Colby Banbury

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    sophiacho1
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    naeemkh
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    Shvetank Prakash
    Shvetank Prakash

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    Vijay Janapa Reddi
    Vijay Janapa Reddi

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    Shvetank Prakash
    Shvetank Prakash

    📖
    Ikechukwu Uchendu
    Ikechukwu Uchendu

    📖
    aptl26
    aptl26

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    Marcelo Rovai
    Marcelo Rovai

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    Henry Bae
    Henry Bae

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    Marco Zennaro
    Marco Zennaro

    📖
    Jayson Lin
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    happyappledog
    happyappledog

    📖
    Vijay Janapa Reddi
    Vijay Janapa Reddi

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    Mark Mazumder
    Mark Mazumder

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    naeemkh
    naeemkh

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    ishapira
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    sophiacho1
    sophiacho1

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    Jeffrey Ma
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    Jessica Quaye

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    sjohri20
    sjohri20

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    Marco Zennaro
    Marco Zennaro

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    sjohri20
    sjohri20

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    aptl26
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    Jayson Lin
    Jayson Lin

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    Ikechukwu Uchendu
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    Matthew Stewart
    Matthew Stewart

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    Colby Banbury
    Colby Banbury

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    From 917cb2e1241d49831fea105e55e475d60521be3e Mon Sep 17 00:00:00 2001 From: Marcelo Rovai Date: Tue, 7 Nov 2023 10:27:39 -0300 Subject: [PATCH 18/59] Adding DSP Chapter. Adding a Preprocessing DSP Chapter. 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Millions of containers are transported daily via various means, such as ships, trucks, and trains, to destinations worldwide. Ensuring these containers' safe and efficient transit is a monumental task that requires leveraging modern technology, and TinyML is undoubtedly one of them. + +In this hands-on tutorial, we will work to solve real-world problems related to transportation. We will develop a Motion Classification and Anomaly Detection system using the Arduino Nicla Vision board, the Arduino IDE, and the Edge Impulse Studio. This project will help us understand how containers experience different forces and motions during various phases of transportation, such as terrestrial and maritime transit, vertical movement via forklifts, and stationary periods in warehouses. + +### Learning Objectives + +1. Setting up the Arduino Nicla Vision Board +2. Data Collection and Preprocessing +3. Building the Motion Classification Model +4. Implementing Anomaly Detection +5. Real-world Testing and Analysis +6. Conclusion and Next Steps + +By the end of this tutorial, you'll have a working prototype that can classify different types of motion and detect anomalies during the transportation of containers. This knowledge can be a stepping stone to more advanced projects in the burgeoning field of TinyML involving vibration. + +## IMU Installation and testing + +For this project, we will use an accelerometer. As discussed in the Hands-On Tutorial, *Setup Nicla Vision*, the Nicla Vision Board has an onboard **6-axis IMU**: 3D gyroscope and 3D accelerometer, the [LSM6DSOX](https://www.st.com/resource/en/datasheet/lsm6dsox.pdf). Let's verify if the [LSM6DSOX IMU library](https://github.com/arduino-libraries/Arduino_LSM6DSOX) is installed. If not, install it. + +![](images/imgs_motion_classif/imu_ide.jpg){fig-align="center" width="6.5in"} + +Next, go to `Examples > Arduino_LSM6DSOX > SimpleAccelerometer` and run the accelerometer test. You can check if it works by opening the IDE Serial Monitor or Plotter. The values are in g (earth gravity), with a default range of +/- 4g: + +![](images/imgs_motion_classif/imu_test.jpg){fig-align="center" width="6.5in"} + +### Defining the Sampling frequency: + +Choosing an appropriate sampling frequency is crucial for capturing the motion characteristics you're interested in studying. The Nyquist-Shannon sampling theorem states that the sampling rate should be at least twice the highest frequency component in the signal to reconstruct it properly. In the context of motion classification and anomaly detection for transportation, the choice of sampling frequency would depend on several factors: + +1. **Nature of the Motion**: Different types of transportation (terrestrial, maritime, etc.) may involve different ranges of motion frequencies. Faster movements may require higher sampling frequencies. + +2. **Hardware Limitations**: The Arduino Nicla Vision board and any associated sensors may have limitations on how fast they can sample data. + +3. **Computational Resources**: Higher sampling rates will generate more data, which might be computationally intensive, especially critical in a TinyML environment. + +4. **Battery Life**: A higher sampling rate will consume more power. If the system is battery-operated, this is an important consideration. + +5. **Data Storage**: More frequent sampling will require more storage space, another crucial consideration for embedded systems with limited memory. + +In many human activity recognition tasks, **sampling rates of around 50 Hz to 100 Hz** are commonly used. Given that we are simulating transportation scenarios, which are generally not high-frequency events, a sampling rate in that range (50-100 Hz) might be a reasonable starting point. + +Let's define a sketch that will allow us to capture our data with a defined sampling frequency (for example, 50Hz): + +``` cpp +/* + * Based on Edge Impulse Data Forwarder Example (Arduino) + - https://docs.edgeimpulse.com/docs/cli-data-forwarder + * Developed by M.Rovai @11May23 + */ + +/* Include ----------------------------------------------------------------- */ +#include + +/* Constant defines -------------------------------------------------------- */ +#define CONVERT_G_TO_MS2 9.80665f +#define FREQUENCY_HZ 50 +#define INTERVAL_MS (1000 / (FREQUENCY_HZ + 1)) + +static unsigned long last_interval_ms = 0; +float x, y, z; + +void setup() { + Serial.begin(9600); + while (!Serial); + + if (!IMU.begin()) { + Serial.println("Failed to initialize IMU!"); + while (1); + } +} + +void loop() { + if (millis() > last_interval_ms + INTERVAL_MS) { + last_interval_ms = millis(); + + if (IMU.accelerationAvailable()) { + // Read raw acceleration measurements from the device + IMU.readAcceleration(x, y, z); + + // converting to m/s2 + float ax_m_s2 = x * CONVERT_G_TO_MS2; + float ay_m_s2 = y * CONVERT_G_TO_MS2; + float az_m_s2 = z * CONVERT_G_TO_MS2; + + Serial.print(ax_m_s2); + Serial.print("\t"); + Serial.print(ay_m_s2); + Serial.print("\t"); + Serial.println(az_m_s2); + } + } +} +``` + +Uploading the sketch and inspecting the Serial Monitor, we can see that we are capturing 50 samples per second. + +![](images/imgs_motion_classif/sampling.jpg){fig-align="center" width="6.5in"} + +> Note that with the Nicla board resting on a table (with the camera facing down), the z-axis measures around 9.8m/s$^2$, the expected earth acceleration. + +## The Case Study: Simulated Container Transportation + +We will simulate container (or better package) transportation through different scenarios to make this tutorial more relatable and practical. Using the built-in accelerometer of the Arduino Nicla Vision board, we'll capture motion data by manually simulating the conditions of: + +1. **Terrestrial** Transportation (by road or train) +2. **Maritime**-associated Transportation +3. Vertical Movement via Fork-**Lift** +4. Stationary **(Idle**) period in a Warehouse + +![](images/imgs_motion_classif/classes.jpg){fig-align="center" width="6.5in"} + +From the above images, we can define for our simulation that primarily horizontal movements (x or y axis) should be associated with the "Terrestrial class," Vertical movements (z-axis) with the "Lift Class," no activity with the "Idle class," and movement on all three axes to [Maritime class.](https://www.containerhandbuch.de/chb_e/stra/index.html?/chb_e/stra/stra_02_03_03.htm) + +![](images/imgs_motion_classif/classes_mov_def.jpg){fig-align="center" width="6.5in"} + +## Data Collection + +For data collection, we can have several options. In a real case, we can have our device, for example, connected directly to one container, and the data collected on a file (for example .CSV) and stored on an SD card (Via SPI connection) or an offline repo in your computer. Data can also be sent remotely to a nearby repository, such as a mobile phone, using Bluetooth (as done in this project: [Sensor DataLogger](https://www.hackster.io/mjrobot/sensor-datalogger-50e44d)). Once your dataset is collected and stored as a .CSV file, it can be uploaded to the Studio using the [CSV Wizard tool](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/csv-wizard). + +> In this [video](https://youtu.be/2KBPq_826WM), you can learn alternative ways to send data to the Edge Impulse Studio. + +### Connecting the device to Edge Impulse + +We will connect the Nicla directly to the Edge Impulse Studio, which will also be used for data pre-processing, model training, testing, and deployment. For that, you have two options: + +1. Download the latest firmware and connect it directly to the `Data Collection` section. +2. Use the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) tool to capture sensor data from the sensor and send it to the Studio. + +Option 1 is more straightforward, as we saw in the *Setup Nicla Vision* hands-on, but option 2 will give you more flexibility regarding capturing your data, such as sampling frequency definition. Let's do it with the last one. + +Please create a new project on the Edge Impulse Studio (EIS) and connect the Nicla to it, following these steps: + +1. Install the [Edge Impulse CLI](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-installation) and the [Node.js](https://nodejs.org/en/) into your computer. +2. Upload a sketch for data capture (the one discussed previously in this tutorial). +3. Use the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) to capture data from the Nicla's accelerometer and send it to the Studio, as shown in this diagram: + +![](images/imgs_motion_classif/data-forw.jpg){fig-align="center" width="5.25in"} + +Start the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) on your terminal, entering (if it is the first time) the following command: + +``` +$ edge-impulse-data-forwarder --clean +``` + +Next, enter your EI credentials and choose your project, variables (for example, *accX,* *accY*, and *accZ*), and device name (for example, *NiclaV*: + +![](images/imgs_motion_classif/term.jpg){fig-align="center" width="6.5in"} + +Go to the `Devices` section on your EI Project and verify if the device is connected (the dot should be green): + +![](images/imgs_motion_classif/device.jpg){fig-align="center" width="6.5in"} + +> You can clone the project developed for this hands-on: [NICLA Vision Movement Classification](https://studio.edgeimpulse.com/public/302078/latest). + +### Data Collection + +On the `Data Acquisition` section, you should see that your board `[NiclaV]` is connected. The sensor is available: `[sensor with 3 axes (accX, accY, accZ)]` with a sampling frequency of `[50Hz]`. The Studio suggests a sample length of `[10000]` ms (10s). The last thing left is defining the sample label. Let's start with`[terrestrial]`: + +![](images/imgs_motion_classif/collect_data.jpg){fig-align="center" width="5.64in"} + +**Terrestrial** (palettes in a Truck or Train), moving horizontally. Press `[Start Sample]`and move your device horizontally, keeping one direction over your table. After 10 s, your data will be uploaded to the studio. Here is how the sample was collected: + +![](images/imgs_motion_classif/terrestrial_result.jpg){fig-align="center" width="6.5in"} + +As expected, the movement was captured mainly in the Y-axis (green). In the blue, we see the Z axis, around -10 m/s$^2$ (the Nicla has the camera facing up). + +As discussed before, we should capture data from all four Transportation Classes. So, imagine that you have a container with a built-in accelerometer facing the following situations: + +**Maritime** (pallets in boats into an angry ocean). The movement is captured on all three axes: + +![](images/imgs_motion_classif/maritime_result.jpg){fig-align="center" width="6.5in"} + +**Lift** (Palettes being handled vertically by a Forklift). Movement captured only in the Z-axis: + +![](images/imgs_motion_classif/lift_result.jpg){fig-align="center" width="6.5in"} + +**Idle** (Paletts in a warehouse). No movement detected by the accelerometer: + +![](images/imgs_motion_classif/idle_result.jpg){fig-align="center" width="6.5in"} + +You can capture, for example, 2 minutes (twelve samples of 10 seconds) for each of the four classes (a total of 8 minutes of data). Using the `three dots` menu after each one of the samples, select 2 of them, reserving them for the Test set. Alternatively, you can use the automatic `Train/Test Split tool` on the `Danger Zone` of `Dashboard` tab. Below, you can see the resulting dataset: + +![](images/imgs_motion_classif/dataset.jpg){fig-align="center" width="6.5in"} + +Once you have captured your dataset, you can explore it in more detail using the [Data Explorer](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/data-explorer), a visual tool to find outliers or mislabeled data (helping to correct them). The data explorer first tries to extract meaningful features from your data (by applying signal processing and neural network embeddings) and then uses a dimensionality reduction algorithm such as [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) or [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) to map these features to a 2D space. This gives you a one-look overview of your complete dataset. + +![](images/imgs_motion_classif/data_explorer.jpg){fig-align="center" width="6.5in"} + +In our case, the dataset seems OK (good separation). But the PCA shows we can have issues between maritime (green) and lift (orange). This is expected, once on a boat, sometimes the movement can be only "vertical". + +## Impulse Design + +The next step is the definition of our Impulse, which takes the raw data and uses signal processing to extract features, passing them as the input tensor of a *learning block* to classify new data. Go to `Impulse Design` and `Create Impulse`. The Studio will suggest the basic design. Let's also add a second *Learning Block* for `Anomaly Detection`. + +![](images/imgs_motion_classif/impulse.jpg){fig-align="center" width="6.5in"} + +This second model uses a K-means model. If we imagine that we could have our known classes as clusters, any sample that could not fit on that could be an outlier, an anomaly such as a container rolling out of a ship on the ocean or falling from a Forklift. + +![](images/imgs_motion_classif/anomaly_detect.jpg){fig-align="center" width="6.5in"} + +The sampling frequency should be automatically captured, if not, enter it: `[50]`Hz. The Studio suggests a *Window Size* of 2 seconds (`[2000]` ms) with a *sliding window* of `[20]`ms. What we are defining in this step is that we will pre-process the captured data (Time-Seres data), creating a tabular dataset features) that will be the input for a Neural Networks Classifier (DNN) and an Anomaly Detection model (K-Means), as shown below: + +![](images/imgs_motion_classif/impulse-block.jpg){fig-align="center" width="6.5in"} + +Let's dig into those steps and parameters to understand better what we are doing here. + +### Data Pre-Processing Overview + +Data pre-processing is extracting features from the dataset captured with the accelerometer, which involves processing and analyzing the raw data. Accelerometers measure the acceleration of an object along one or more axes (typically three, denoted as X, Y, and Z). These measurements can be used to understand various aspects of the object's motion, such as movement patterns and vibrations. + +Raw accelerometer data can be noisy and contain errors or irrelevant information. Preprocessing steps, such as filtering and normalization, can clean and standardize the data, making it more suitable for feature extraction. In our case, we should divide the data into smaller segments or **windows**. This can help focus on specific events or activities within the dataset, making feature extraction more manageable and meaningful. The **window size** and overlap (**window increase**) choice depend on the application and the frequency of the events of interest. As a thumb rule, we should try to capture a couple of "cycles of data". + +> With a sampling rate (SR) of 50Hz and a window size of 2 seconds, we will get 100 samples per axis, or 300 in total (3 axis x 2 seconds x 50 samples). We will slide this window every 200ms, creating a larger dataset where each instance has 300 raw features. + +![](images/imgs_motion_classif/pre-process.jpg){fig-align="center" width="6.5in"} + +Once the data is preprocessed and segmented, you can extract features that describe the motion's characteristics. Some typical features extracted from accelerometer data include: + +- **Time-domain** features describe the data's statistical properties within each segment, such as mean, median, standard deviation, skewness, kurtosis, and zero-crossing rate. +- **Frequency-domain** features are obtained by transforming the data into the frequency domain using techniques like the Fast Fourier Transform (FFT). Some typical frequency-domain features include the power spectrum, spectral energy, dominant frequencies (amplitude and frequency), and spectral entropy. +- **Time-frequency** domain features combine the time and frequency domain information, such as the Short-Time Fourier Transform (STFT) or the Discrete Wavelet Transform (DWT). They can provide a more detailed understanding of how the signal's frequency content changes over time. + +In many cases, the number of extracted features can be large, which may lead to overfitting or increased computational complexity. Feature selection techniques, such as mutual information, correlation-based methods, or principal component analysis (PCA), can help identify the most relevant features for a given application and reduce the dimensionality of the dataset. The Studio can help with such feature importance calculations. + +### EI Studio Spectral Features + +Data preprocessing is a challenging area for embedded machine learning, still, Edge Impulse helps overcome this with its digital signal processing (DSP) preprocessing step and, more specifically, the [Spectral Features Block](https://docs.edgeimpulse.com/docs/edge-impulse-studio/processing-blocks/spectral-features). + +On the Studio, the collected raw dataset will be the input of a Spectral Analysis block, which is excellent for analyzing repetitive motion, such as data from accelerometers. This block will perform a DSP (Digital Signal Processing), extracting features such as [FFT](https://en.wikipedia.org/wiki/Fast_Fourier_transform) or [Wavelets](https://en.wikipedia.org/wiki/Digital_signal_processing#Wavelet). + +For our project, once the time signal is continuous, we should use FFT with, for example, a length of `[32]`. + +The per axis/channel **Time Domain Statistical features** are: + +- [RMS](https://en.wikipedia.org/wiki/Root_mean_square): 1 feature +- [Skewness](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FSkewness): 1 feature +- [Kurtosis](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FKurtosis): 1 feature + +The per axis/channel **Frequency Domain Spectral features** are: + +- [Spectral Power](https://en.wikipedia.org/wiki/Spectral_density): 16 features (FFT Length/2) +- Skewness: 1 feature +- Kurtosis: 1 feature + +So, for an FFT length of 32 points, the resulting output of the Spectral Analysis Block will be 21 features per axis (a total of 63 features). + +> You can learn more about how each feature is calculated by downloading the notebook [Edge Impulse - Spectral Features Block Analysis](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Motion_Classification/Edge_Impulse_Spectral_Features_Block.ipynb) [TinyML under the hood: Spectral Analysis](https://www.hackster.io/mjrobot/tinyml-under-the-hood-spectral-analysis-94676c) or [opening it directly on Google CoLab](https://colab.research.google.com/github/Mjrovai/Arduino_Nicla_Vision/blob/main/Motion_Classification/Edge_Impulse_Spectral_Features_Block.ipynb). + +### Generating features + +Once we understand what the pre-processing does, it is time to finish the job. So, let's take the raw data (time-series type) and convert it to tabular data. For that, go to the `Spectral Features` section on the `Parameters` tab, define the main parameters as discussed in the previous section (`[FFT]` with `[32]` points), and select`[Save Parameters]`: + +![](images/imgs_motion_classif/Parameters_definition.jpg){fig-align="center" width="6.5in"} + +At the top menu, select the `Generate Features` option and the `Generate Features` button. Each 2-second window data will be converted into one data point of 63 features. + +> The Feature Explorer will show those data in 2D using [UMAP.](https://umap-learn.readthedocs.io/en/latest/) Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE but is also applicable for general non-linear dimension reduction. + +The visualization makes it possible to verify that after the feature generation, the classes present keep their excellent separation, which indicates that the classifier should work well. Optionally, you can analyze how important each one of the features is for one class compared with others. + +![](images/imgs_motion_classif/feature_generation.jpg){fig-align="center" width="6.5in"} + +## Models Training + +Our classifier will be a Dense Neural Network (DNN) that will have 63 neurons on its input layer, two hidden layers with 20 and 10 neurons, and an output layer with four neurons (one per each class), as shown here: + +![](images/imgs_motion_classif/model.jpg){fig-align="center" width="6.5in"} + +As hyperparameters, we will use a Learning Rate of `[0.005]`, a Batch size of `[32]`, and `[20]`% of data for validation for `[30]` epochs. After training, we can see that the accuracy is 98.5%. The cost of memory and latency is meager. + +![](images/imgs_motion_classif/train.jpg){fig-align="center" width="6.5in"} + +For Anomaly Detection, we will choose the suggested features that are precisely the most important ones in the Feature Extraction, plus the accZ RMS. The number of clusters will be `[32]`, as suggested by the Studio: + +![](images/imgs_motion_classif/anom_det_train.jpg){fig-align="center" width="6.5in"} + +## Testing + +We can verify how our model will behave with unknown data using 20% of the data left behind during the data capture phase. The result was almost 95%, which is good. You can always work to improve the results, for example, to understand what went wrong with one of the wrong results. If it is a unique situation, you can add it to the training dataset and then repeat it. + +The default minimum threshold for a considered uncertain result is `[0.6]` for classification and `[0.3]` for anomaly. Once we have four classes (their output sum should be 1.0), you can also set up a lower threshold for a class to be considered valid (for example, 0.4). You can `Set confidence thresholds` on the `three dots` menu, besides the `Classy all` button. + +![](images/imgs_motion_classif/model_testing.jpg){fig-align="center" width="6.5in"} + +You can also perform Live Classification with your device (which should still be connected to the Studio). + +> Be aware that here, you will capture real data with your device and upload it to the Studio, where an inference will be taken using the trained model (But the **model is NOT in your device**). + +## Deploy + +It is time to deploy the preprocessing block and the trained model to the Nicla. The Studio will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. You should select the option `Arduino Library`, and at the bottom, you can choose `Quantized (Int8)` or `Unoptimized (float32)` and `[Build]`. A Zip file will be created and downloaded to your computer. + +![](images/imgs_motion_classif/deploy.jpg){fig-align="center" width="6.5in"} + +On your Arduino IDE, go to the `Sketch` tab, select `Add.ZIP Library`, and Choose the.zip file downloaded by the Studio. A message will appear in the IDE Terminal: `Library installed`. + +### Inference + +Now, it is time for a real test. We will make inferences wholly disconnected from the Studio. Let's change one of the code examples created when you deploy the Arduino Library. + +In your Arduino IDE, go to the `File/Examples` tab and look for your project, and on examples, select `Nicla_vision_fusion`: + +![](images/imgs_motion_classif/inference.jpg){fig-align="center" width="6.5in"} + +Note that the code created by Edge Impulse considers a *sensor fusion* approach where the IMU (Accelerometer and Gyroscope) and the ToF are used. At the beginning of the code, you have the libraries related to our project, IMU and ToF: + +``` cpp +/* Includes ---------------------------------------------------------------- */ +#include +#include //IMU +#include "VL53L1X.h" // ToF +``` + +> You can keep the code this way for testing because the trained model will use only features pre-processed from the accelerometer. But consider that you will write your code only with the needed libraries for a real project. + +And that is it! + +You can now upload the code to your device and proceed with the inferences. Press the Nicla `[RESET]` button twice to put it on boot mode (disconnect from the Studio if it is still connected), and upload the sketch to your board. + +Now you should try different movements with your board (similar to those done during data capture), observing the inference result of each class on the Serial Monitor: + +- **Idle and lift classes:** + +![](images/imgs_motion_classif/inference_1.jpg){fig-align="center" width="6.5in"} + +- **maritime and terrestrial**: + +![](images/imgs_motion_classif/inference_2.jpg){fig-align="center" width="6.5in"} + +Note that in all situations above, the value of the `anomaly score` was smaller than 0.0. Try a new movement that was not part of the original dataset, for example, "rolling" the Nicla, facing the camera upside-down, as a container falling from a boat or even a boat accident: + +- **anomaly detection**: + +![](images/imgs_motion_classif/anomaly-boat.jpg){fig-align="center" width="6.5in"} + +In this case, the anomaly is much bigger, over 1.00 + +### Post-processing + +Now that we know the model is working since it detects the movements, we suggest that you modify the code to see the result with the NiclaV completely offline (disconnected from the PC and powered by a battery, a power bank, or an independent 5V power supply). + +The idea is to do the same as with the KWS project: if one specific movement is detected, a specific LED could be lit. For example, if *terrestrial* is detected, the Green LED will light; if *maritime*, the Red LED will light, if it is a *lift,* the Blue LED will light; and if no movement is detected *(idle*), the LEDs will be OFF. You can also add a condition when an anomaly is detected, in this case, for example, a white color can be used (all e LEDs light simultaneously). + +## Conclusion + +> The notebooks and codes used in this hands-on tutorial will be found on the [GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/Motion_Classification) repository. + +Before we finish, consider that Movement Classification and Object Detection can be utilized in many applications across various domains. Here are some of the potential applications: + +### Case Applications + +#### Industrial and Manufacturing + +- **Predictive Maintenance**: Detecting anomalies in machinery motion to predict failures before they occur. +- **Quality Control**: Monitoring the motion of assembly lines or robotic arms for precision assessment and deviation detection from the standard motion pattern. +- **Warehouse Logistics**: Managing and tracking the movement of goods with automated systems that classify different types of motion and detect anomalies in handling. + +#### Healthcare + +- **Patient Monitoring**: Detecting falls or abnormal movements in the elderly or those with mobility issues. +- **Rehabilitation**: Monitoring the progress of patients recovering from injuries by classifying motion patterns during physical therapy sessions. +- **Activity Recognition**: Classifying types of physical activity for fitness applications or patient monitoring. + +#### Consumer Electronics + +- **Gesture Control**: Interpreting specific motions to control devices, such as turning on lights with a hand wave. +- **Gaming**: Enhancing gaming experiences with motion-controlled inputs. + +#### Transportation and Logistics + +- **Vehicle Telematics**: Monitoring vehicle motion for unusual behavior such as hard braking, sharp turns, or accidents. +- **Cargo Monitoring**: Ensuring the integrity of goods during transport by detecting unusual movements that could indicate tampering or mishandling. + +#### Smart Cities and Infrastructure + +- **Structural Health Monitoring**: Detecting vibrations or movements within structures that could indicate potential failures or maintenance needs. +- **Traffic Management**: Analyzing the flow of pedestrians or vehicles to improve urban mobility and safety. + +#### Security and Surveillance + +- **Intruder Detection**: Detecting motion patterns typical of unauthorized access or other security breaches. +- **Wildlife Monitoring**: Detecting poachers or abnormal animal movements in protected areas. + +#### Agriculture + +- **Equipment Monitoring**: Tracking the performance and usage of agricultural machinery. +- **Animal Behavior Analysis**: Monitoring livestock movements to detect behaviors indicating health issues or stress. + +#### Environmental Monitoring + +- **Seismic Activity**: Detecting irregular motion patterns that precede earthquakes or other geologically relevant events. +- **Oceanography**: Studying wave patterns or marine movements for research and safety purposes. + +### Nicla 3D case + +For real applications, as some described before, we can add a case to our device, and Eoin Jordan, from Edge Impulse, developed a great wearable and machine health case for the Nicla range of boards. It works with a 10mm magnet, 2M screws, and a 16mm strap for human and machine health use case scenarios. Here is the link: [Arduino Nicla Voice and Vision Wearable Case](https://www.thingiverse.com/thing:5923305). + +![](images/imgs_motion_classif/case.jpg){fig-align="center" width="6.5in"} + +The applications for motion classification and anomaly detection are extensive, and the Arduino Nicla Vision is well-suited for scenarios where low power consumption and edge processing are advantageous. Its small form factor and efficiency in processing make it an ideal choice for deploying portable and remote applications where real-time processing is crucial and connectivity may be limited. diff --git a/_quarto.yml b/_quarto.yml index 42d399fc..cc9504e7 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -81,6 +81,7 @@ book: - object_detection_fomo.qmd - kws_feature_eng.qmd - kws_nicla.qmd + - dsp_spectral_features_block.qmd - motion_classify_ad.qmd references: references.qmd diff --git a/dsp_spectral_features_block.qmd b/dsp_spectral_features_block.qmd new file mode 100644 index 00000000..64126027 --- /dev/null +++ b/dsp_spectral_features_block.qmd @@ -0,0 +1,613 @@ +# DSP - Spectral Features {.unnumbered} + +## Introduction + +TinyML projects related to motion (or vibration) involve data from IMUs (usually **accelerometers** and **gyroscopes**). These time-series type datasets should be preprocessed before inputting them into a Machine Learning model training, which is a challenging area for embedded machine learning. Still, Edge Impulse helps overcome this complexity with its digital signal processing (DSP) preprocessing step and, more specifically, the [Spectral Features Block](https://docs.edgeimpulse.com/docs/edge-impulse-studio/processing-blocks/spectral-features) for Inertial sensors. + +But how does it work under the hood? Let's dig into it. + +## Extracting Features Review + +Extracting features from a dataset captured with inertial sensors, such as accelerometers, involves processing and analyzing the raw data. Accelerometers measure the acceleration of an object along one or more axes (typically three, denoted as X, Y, and Z). These measurements can be used to understand various aspects of the object's motion, such as movement patterns and vibrations. Here's a high-level overview of the process: + +**Data collection**: First, we need to gather data from the accelerometers. Depending on the application, data may be collected at different sampling rates. It's essential to ensure that the sampling rate is high enough to capture the relevant dynamics of the studied motion (the sampling rate should be at least double the maximum relevant frequency present in the signal). + +**Data preprocessing**: Raw accelerometer data can be noisy and contain errors or irrelevant information. Preprocessing steps, such as filtering and normalization, can help clean and standardize the data, making it more suitable for feature extraction. + +> The Studio does not perform normalization or standardization, so sometimes, when working with Sensor Fusion, it could be necessary to perform this step before uploading data to the Studio. This is particularly crucial in sensor fusion projects, as seen in this tutorial, [Sensor Data Fusion with Spresense and CommonSense](https://docs.edgeimpulse.com/experts/air-quality-and-environmental-projects/environmental-sensor-fusion-commonsense). + +**Segmentation**: Depending on the nature of the data and the application, dividing the data into smaller segments or **windows** may be necessary. This can help focus on specific events or activities within the dataset, making feature extraction more manageable and meaningful. The **window size** and overlap (**window span**) choice depend on the application and the frequency of the events of interest. As a rule of thumb, we should try to capture a couple of "data cycles." + +**Feature extraction**: Once the data is preprocessed and segmented, you can extract features that describe the motion's characteristics. Some typical features extracted from accelerometer data include: + +- **Time-domain** features describe the data's [statistical properties](https://www.mdpi.com/1424-8220/22/5/2012) within each segment, such as mean, median, standard deviation, skewness, kurtosis, and zero-crossing rate. +- **Frequency-domain** features are obtained by transforming the data into the frequency domain using techniques like the [Fast Fourier Transform (FFT)](https://en.wikipedia.org/wiki/Fast_Fourier_transform). Some typical frequency-domain features include the power spectrum, spectral energy, dominant frequencies (amplitude and frequency), and spectral entropy. +- **Time-frequency** domain features combine the time and frequency domain information, such as the [Short-Time Fourier Transform (STFT)](https://en.wikipedia.org/wiki/Short-time_Fourier_transform) or the [Discrete Wavelet Transform (DWT)](https://en.wikipedia.org/wiki/Discrete_wavelet_transform). They can provide a more detailed understanding of how the signal's frequency content changes over time. + +In many cases, the number of extracted features can be large, which may lead to overfitting or increased computational complexity. Feature selection techniques, such as mutual information, correlation-based methods, or principal component analysis (PCA), can help identify the most relevant features for a given application and reduce the dimensionality of the dataset. The Studio can help with such feature-relevant calculations. + +Let's explore in more detail a typical TinyML Motion Classification project covered in this series of Hands-Ons. + +## A TinyML Motion Classification project + +![](images/imgs_dsp_spectral_features/spectral_block.jpeg){fig-align="center" width="6.5in"} + +In the hands-on project, *Motion Classification and Anomaly Detection*, we simulated mechanical stresses in transport, where our problem was to classify four classes of movement: + +- **Maritime** (pallets in boats) +- **Terrestrial** (pallets in a Truck or Train) +- **Lift** (pallets being handled by Fork-Lift) +- **Idle** (pallets in Storage houses) + +The accelerometers provided the data on the pallet (or container). + +![](images/imgs_dsp_spectral_features/case_study.png){fig-align="center" width="6.5in"} + +Below is one sample (raw data) of 10 seconds, captured with a sampling frequency of 50Hz: + +![](images/imgs_dsp_spectral_features/data_sample.png){fig-align="center" width="6.5in"} + +> The result is similar when this analysis is done over another dataset with the same principle, using a different sampling frequency, 62.5Hz instead of 50Hz. + +## Data Pre-Processing + +The raw data captured by the accelerometer (a "time series" data) should be converted to "tabular data" using one of the typical Feature Extraction methods described in the last section. + +We should segment the data using a sliding window over the sample data for feature extraction. The project captured accelerometer data every 10 seconds with a sample rate of 62.5 Hz. A 2-second window captures 375 data points (3 axis x 2 seconds x 62.5 samples). The window is slid every 80ms, creating a larger dataset where each instance has 375 "raw features." + +![](images/imgs_dsp_spectral_features/v1.png){fig-align="center" width="6.5in"} + +On the Studio, the previous version (V1) of the **Spectral Analysis Block** extracted as time-domain features only the RMS, and for the frequency-domain, the peaks and frequency (using FFT) and the power characteristics (PSD) of the signal over time resulting in a fixed tabular dataset of 33 features (11 per each axis), + +![](images/imgs_dsp_spectral_features/v1_features.png){fig-align="center" width="6.5in"} + +Those 33 features were the Input tensor of a Neural Network Classifier. + +In 2022, Edge Impulse released version 2 of the Spectral Analysis block, which we will explore here. + +### Edge Impulse - Spectral Analysis Block V.2 under the hood + +In Version 2, Time Domain Statistical features per axis/channel are: + +- RMS +- Skewness +- Kurtosis + +And the Frequency Domain Spectral features per axis/channel are: + +- Spectral Power +- Skewness (in the next version) +- Kurtosis (in the next version) + +In this [link,](https://docs.edgeimpulse.com/docs/edge-impulse-studio/processing-blocks/spectral-features) we can have more details about the feature extraction. + +> Clone the [public project](https://studio.edgeimpulse.com/public/198358/latest). You can also follow the explanation, playing with the code using my Google CoLab Notebook: [Edge Impulse Spectral Analysis Block Notebook](https://colab.research.google.com/github/Mjrovai/TinyML4D/blob/main/SciTinyM-2023/Edge_Impulse-Spectral_Analysis_Block/Edge_Impulse_Spectral_Analysis_Block_V3.ipynb). + +Start importing the libraries: + +``` python +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import math +from scipy.stats import skew, kurtosis +from scipy import signal +from scipy.signal import welch +from scipy.stats import entropy +from sklearn import preprocessing +import pywt + +plt.rcParams['figure.figsize'] = (12, 6) +plt.rcParams['lines.linewidth'] = 3 +``` + +From the studied project, let's choose a data sample from accelerometers as below: + +- Window size of 2 seconds: `[2,000]` ms +- Sample frequency: `[62.5]` Hz +- We will choose the `[None]` filter (for simplicity) and a +- FFT length: `[16]`. + +``` python +f = 62.5 # Hertz +wind_sec = 2 # seconds +FFT_Lenght = 16 +axis = ['accX', 'accY', 'accZ'] +n_sensors = len(axis) +``` + +![](images/imgs_dsp_spectral_features/impulse.png){fig-align="center" width="5.6in"} + +Selecting the *Raw Features* on the Studio Spectral Analysis tab, we can copy all 375 data points of a particular 2-second window to the clipboard. + +![](images/imgs_dsp_spectral_features/features.png){fig-align="center" width="6.5in"} + +Paste the data points to a new variable *data*: + +``` python +data=[-5.6330, 0.2376, 9.8701, -5.9442, 0.4830, 9.8701, -5.4217, ...] +No_raw_features = len(data) +N = int(No_raw_features/n_sensors) +``` + +The total raw features are 375, but we will work with each axis individually, where N= 125 (number of samples per axis). + +We aim to understand how Edge Impulse gets the processed features. + +![](images/imgs_dsp_spectral_features/process_features.png){fig-align="center" width="4.57in"} + +So, you should also past the processed features on a variable (to compare the calculated features in Python with the ones provided by the Studio) : + +``` python +features = [2.7322, -0.0978, -0.3813, 2.3980, 3.8924, 24.6841, 9.6303, ...] +N_feat = len(features) +N_feat_axis = int(N_feat/n_sensors) +``` + +The total number of processed features is 39, which means 13 features/axis. + +Looking at those 13 features closely, we will find 3 for the time domain (RMS, Skewness, and Kurtosis): + +- `[rms] [skew] [kurtosis]` + +and 10 for the frequency domain (we will return to this later). + +- `[spectral skew][spectral kurtosis][Spectral Power 1] ... [Spectral Power 8]` + +**Splitting raw data per sensor** + +The data has samples from all axes; let's split and plot them separately: + +``` python +def plot_data(sensors, axis, title): + [plt.plot(x, label=y) for x,y in zip(sensors, axis)] + plt.legend(loc='lower right') + plt.title(title) + plt.xlabel('#Sample') + plt.ylabel('Value') + plt.box(False) + plt.grid() + plt.show() + +accX = data[0::3] +accY = data[1::3] +accZ = data[2::3] +sensors = [accX, accY, accZ] +plot_data(sensors, axis, 'Raw Features') +``` + +![](images/imgs_dsp_spectral_features/sample.png){fig-align="center" width="6.5in"} + +**Subtracting the mean** + +Next, we should subtract the mean from the *data*. Subtracting the mean from a data set is a common data pre-processing step in statistics and machine learning. The purpose of subtracting the mean from the data is to center the data around zero. This is important because it can reveal patterns and relationships that might be hidden if the data is not centered. + +Here are some specific reasons why subtracting the mean can be helpful: + +- It simplifies analysis: By centering the data, the mean becomes zero, making some calculations simpler and easier to interpret. +- It removes bias: If the data is biased, subtracting the mean can remove it and allow for a more accurate analysis. +- It can reveal patterns: Centering the data can help uncover patterns that might be hidden if the data is not centered. For example, centering the data can help you identify trends over time if you analyze a time series dataset. +- It can improve performance: In some machine learning algorithms, centering the data can improve performance by reducing the influence of outliers and making the data more easily comparable. Overall, subtracting the mean is a simple but powerful technique that can be used to improve the analysis and interpretation of data. + +``` python +dtmean = [(sum(x)/len(x)) for x in sensors] +[print('mean_'+x+'= ', round(y, 4)) for x,y in zip(axis, dtmean)][0] + +accX = [(x - dtmean[0]) for x in accX] +accY = [(x - dtmean[1]) for x in accY] +accZ = [(x - dtmean[2]) for x in accZ] +sensors = [accX, accY, accZ] + +plot_data(sensors, axis, 'Raw Features - Subctract the Mean') +``` + +![](images/imgs_dsp_spectral_features/sample_no_mean.png){fig-align="center" width="6.5in"} + +## Time Domain Statistical features + +**RMS Calculation** + +The RMS value of a set of values (or a continuous-time waveform) is the square root of the arithmetic mean of the squares of the values or the square of the function that defines the continuous waveform. In physics, the RMS value of an electrical current is defined as the "value of the direct current that dissipates the same power in a resistor." + +In the case of a set of n values {𝑥1, 𝑥2, ..., 𝑥𝑛}, the RMS is: + +![](images/imgs_dsp_spectral_features/rms.png){fig-align="center"} + +> NOTE that the RMS value is different for the original raw data, and after subtracting the mean + +``` py +# Using numpy and standartized data (subtracting mean) +rms = [np.sqrt(np.mean(np.square(x))) for x in sensors] +``` + +We can compare the calculated RMS values here with the ones presented by Edge Impulse: + +``` python +[print('rms_'+x+'= ', round(y, 4)) for x,y in zip(axis, rms)][0] +print("\nCompare with Edge Impulse result features") +print(features[0:N_feat:N_feat_axis]) +``` + +`rms_accX= 2.7322` + +`rms_accY= 0.7833` + +`rms_accZ= 0.1383` + +Compared with Edge Impulse result features: + +`[2.7322, 0.7833, 0.1383]` + +**Skewness and kurtosis calculation** + +In statistics, skewness and kurtosis are two ways to measure the **shape of a distribution**. + +Here, we can see the sensor values distribution: + +``` python +fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(13, 4)) +sns.kdeplot(accX, fill=True, ax=axes[0]) +sns.kdeplot(accY, fill=True, ax=axes[1]) +sns.kdeplot(accZ, fill=True, ax=axes[2]) +axes[0].set_title('accX') +axes[1].set_title('accY') +axes[2].set_title('accZ') +plt.suptitle('IMU Sensors distribution', fontsize=16, y=1.02) +plt.show() +``` + +![](images/imgs_dsp_spectral_features/skew.png){fig-align="center" width="6.5in"} + +[**Skewness**](https://en.wikipedia.org/wiki/Skewness) is a measure of the asymmetry of a distribution. This value can be positive or negative. + +![](images/imgs_dsp_spectral_features/skew_2.png){fig-align="center" width="4.65in"} + +- A negative skew indicates that the tail is on the left side of the distribution, which extends towards more negative values. +- A positive skew indicates that the tail is on the right side of the distribution, which extends towards more positive values. +- A zero value indicates no skewness in the distribution at all, meaning the distribution is perfectly symmetrical. + +``` python +skew = [skew(x, bias=False) for x in sensors] +[print('skew_'+x+'= ', round(y, 4)) for x,y in zip(axis, skew)][0] +print("\nCompare with Edge Impulse result features") +features[1:N_feat:N_feat_axis] +``` + +`skew_accX= -0.099` + +`skew_accY= 0.1756` + +`skew_accZ= 6.9463` + +Compared with Edge Impulse result features: + +`[-0.0978, 0.1735, 6.8629]` + +[**Kurtosis**](https://en.wikipedia.org/wiki/Kurtosis) is a measure of whether or not a distribution is heavy-tailed or light-tailed relative to a normal distribution. + +![](images/imgs_dsp_spectral_features/kurto.png){fig-align="center"} + +- The kurtosis of a normal distribution is zero. +- If a given distribution has a negative kurtosis, it is said to be playkurtic, which means it tends to produce fewer and less extreme outliers than the normal distribution. +- If a given distribution has a positive kurtosis , it is said to be leptokurtic, which means it tends to produce more outliers than the normal distribution. + +``` python +kurt = [kurtosis(x, bias=False) for x in sensors] +[print('kurt_'+x+'= ', round(y, 4)) for x,y in zip(axis, kurt)][0] +print("\nCompare with Edge Impulse result features") +features[2:N_feat:N_feat_axis] +``` + +`kurt_accX= -0.3475` + +`kurt_accY= 1.2673` + +`kurt_accZ= 68.1123` + +Compared with Edge Impulse result features: + +`[-0.3813, 1.1696, 65.3726]` + +## Spectral features + +The filtered signal is passed to the Spectral power section, which computes the **FFT** to generate the spectral features. + +Since the sampled window is usually larger than the FFT size, the window will be broken into frames (or "sub-windows"), and the FFT is calculated over each frame. + +**FFT length** - The FFT size. This determines the number of FFT bins and the resolution of frequency peaks that can be separated. A low number means more signals will average together in the same FFT bin, but it also reduces the number of features and model size. A high number will separate more signals into separate bins, generating a larger model. + +- The total number of Spectral Power features will vary depending on how you set the filter and FFT parameters. With No filtering, the number of features is 1/2 of the FFT Length. + +**Spectral Power - Welch's method** + +We should use [Welch's method](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.welch.html) to split the signal on the frequency domain in bins and calculate the power spectrum for each bin. This method divides the signal into overlapping segments, applies a window function to each segment, computes the periodogram of each segment using DFT, and averages them to obtain a smoother estimate of the power spectrum. + +``` python +# Function used by Edge Impulse instead of scipy.signal.welch(). +def welch_max_hold(fx, sampling_freq, nfft, n_overlap): + n_overlap = int(n_overlap) + spec_powers = [0 for _ in range(nfft//2+1)] + ix = 0 + while ix <= len(fx): + # Slicing truncates if end_idx > len, and rfft will auto-zero pad + fft_out = np.abs(np.fft.rfft(fx[ix:ix+nfft], nfft)) + spec_powers = np.maximum(spec_powers, fft_out**2/nfft) + ix = ix + (nfft-n_overlap) + return np.fft.rfftfreq(nfft, 1/sampling_freq), spec_powers +``` + +Applying the above function to 3 signals: + +``` python +fax,Pax = welch_max_hold(accX, fs, FFT_Lenght, 0) +fay,Pay = welch_max_hold(accY, fs, FFT_Lenght, 0) +faz,Paz = welch_max_hold(accZ, fs, FFT_Lenght, 0) +specs = [Pax, Pay, Paz ] +``` + +We can plot the Power Spectrum P(f): + +``` python +plt.plot(fax,Pax, label='accX') +plt.plot(fay,Pay, label='accY') +plt.plot(faz,Paz, label='accZ') +plt.legend(loc='upper right') +plt.xlabel('Frequency (Hz)') +#plt.ylabel('PSD [V**2/Hz]') +plt.ylabel('Power') +plt.title('Power spectrum P(f) using Welch\'s method') +plt.grid() +plt.box(False) +plt.show() +``` + +![](images/imgs_dsp_spectral_features/fft.png){fig-align="center" width="6.5in"} + +Besides the Power Spectrum, we can also include the skewness and kurtosis of the features in the frequency domain (should be available on a new version): + +``` python +spec_skew = [skew(x, bias=False) for x in specs] +spec_kurtosis = [kurtosis(x, bias=False) for x in specs] +``` + +Let's now list all Spectral features per axis and compare them with EI: + +``` python +print("EI Processed Spectral features (accX): ") +print(features[3:N_feat_axis][0:]) +print("\nCalculated features:") +print (round(spec_skew[0],4)) +print (round(spec_kurtosis[0],4)) +[print(round(x, 4)) for x in Pax[1:]][0] +``` + +EI Processed Spectral features (accX): + +2.398, 3.8924, 24.6841, 9.6303, 8.4867, 7.7793, 2.9963, 5.6242, 3.4198, 4.2735 + +Calculated features: + +2.9069 8.5569 24.6844 9.6304 8.4865 7.7794 2.9964 5.6242 3.4198 4.2736 + +``` python +print("EI Processed Spectral features (accY): ") +print(features[16:26][0:]) #13: 3+N_feat_axis; 26 = 2x N_feat_axis +print("\nCalculated features:") +print (round(spec_skew[1],4)) +print (round(spec_kurtosis[1],4)) +[print(round(x, 4)) for x in Pay[1:]][0] +``` + +EI Processed Spectral features (accY): + +0.9426, -0.8039, 5.429, 0.999, 1.0315, 0.9459, 1.8117, 0.9088, 1.3302, 3.112 + +Calculated features: + +1.1426 -0.3886 5.4289 0.999 1.0315 0.9458 1.8116 0.9088 1.3301 3.1121 + +``` python +print("EI Processed Spectral features (accZ): ") +print(features[29:][0:]) #29: 3+(2*N_feat_axis); +print("\nCalculated features:") +print (round(spec_skew[2],4)) +print (round(spec_kurtosis[2],4)) +[print(round(x, 4)) for x in Paz[1:]][0] +``` + +EI Processed Spectral features (accZ): + +0.3117, -1.3812, 0.0606, 0.057, 0.0567, 0.0976, 0.194, 0.2574, 0.2083, 0.166 + +Calculated features: + +0.3781 -1.4874 0.0606 0.057 0.0567 0.0976 0.194 0.2574 0.2083 0.166 + +## Time-frequency domain + +### Wavelets + +[Wavelet](https://en.wikipedia.org/wiki/Wavelet) is a powerful technique for analyzing signals with transient features or abrupt changes, such as spikes or edges, which are difficult to interpret with traditional Fourier-based methods. + +Wavelet transforms work by breaking down a signal into different frequency components and analyzing them individually. The transformation is achieved by convolving the signal with a **wavelet function**, a small waveform centered at a specific time and frequency. This process effectively decomposes the signal into different frequency bands, each of which can be analyzed separately. + +One of the critical benefits of wavelet transforms is that they allow for time-frequency analysis, which means that they can reveal the frequency content of a signal as it changes over time. This makes them particularly useful for analyzing non-stationary signals, which vary over time. + +Wavelets have many practical applications, including signal and image compression, denoising, feature extraction, and image processing. + +Let's select Wavelet on the Spectral Features block in the same project: + +- Type: Wavelet +- Wavelet Decomposition Level: 1 +- Wavelet: bior1.3 + +![](images/imgs_dsp_spectral_features/fft_result.png){fig-align="center"} + +**The Wavelet Function** + +``` python +wavelet_name='bior1.3' +num_layer = 1 + +wavelet = pywt.Wavelet(wavelet_name) +[phi_d,psi_d,phi_r,psi_r,x] = wavelet.wavefun(level=5) +plt.plot(x, psi_d, color='red') +plt.title('Wavelet Function') +plt.ylabel('Value') +plt.xlabel('Time') +plt.grid() +plt.box(False) +plt.show() +``` + +![](images/imgs_dsp_spectral_features/wav.png){fig-align="center" width="6.5in"} + +As we did before, let's copy and past the Processed Features: + +![](images/imgs_dsp_spectral_features/wav_processed.png){fig-align="center" width="6.5in"} + +``` python +features = [3.6251, 0.0615, 0.0615, -7.3517, -2.7641, 2.8462, 5.0924, ...] +N_feat = len(features) +N_feat_axis = int(N_feat/n_sensors) +``` + +Edge Impulse computes the [Discrete Wavelet Transform (DWT)](https://pywavelets.readthedocs.io/en/latest/ref/dwt-discrete-wavelet-transform.html) for each one of the Wavelet Decomposition levels selected. After that, the features will be extracted. + +In the case of **Wavelets**, the extracted features are *basic statistical values*, *crossing values*, and *entropy.* There are, in total, 14 features per layer as below: + +- \[11\] Statiscal Features: **n5, n25, n75, n95, mean, median,** standard deviation **(std)**, variance **(var)** root mean square **(rms), kurtosis**, and skewness **(skew)**. +- \[2\] Crossing Features: Zero crossing rate **(zcross)** and mean crossing rate **(mcross)** are the times that the signal passes through the baseline (y = 0) and the average level (y = u) per unit of time, respectively +- \[1\] Complexity Feature: **Entropy** is a characteristic measure of the complexity of the signal + +All the above 14 values are calculated for each Layer (including L0, the original signal) + +- The total number of features varies depending on how you set the filter and the number of layers. For example, with \[None\] filtering and Level\[1\], the number of features per axis will be 14 x 2 (L0 and L1) = 28. For the three axes, we will have a total of 84 features. + +### Wavelet Analysis + +Wavelet analysis decomposes the signal (**accX, accY**, **and accZ**) into different frequency components using a set of filters, which separate these components into low-frequency (slowly varying parts of the signal containing long-term patterns), such as **accX_l1, accY_l1, accZ_l1** and, high-frequency (rapidly varying parts of the signal containing short-term patterns) components, such as **accX_d1, accY_d1, accZ_d1**, permitting the extraction of features for further analysis or classification. + +Only the low-frequency components (approximation coefficients, or cA) will be used. In this example, we assume only one level (Single-level Discrete Wavelet Transform), where the function will return a tuple. With a multilevel decomposition, the "Multilevel 1D Discrete Wavelet Transform", the result will be a list (for detail, please see: [Discrete Wavelet Transform (DWT)](https://pywavelets.readthedocs.io/en/latest/ref/dwt-discrete-wavelet-transform.html) ) + +``` python +(accX_l1, accX_d1) = pywt.dwt(accX, wavelet_name) +(accY_l1, accY_d1) = pywt.dwt(accY, wavelet_name) +(accZ_l1, accZ_d1) = pywt.dwt(accZ, wavelet_name) +sensors_l1 = [accX_l1, accY_l1, accZ_l1] + +# Plot power spectrum versus frequency +plt.plot(accX_l1, label='accX') +plt.plot(accY_l1, label='accY') +plt.plot(accZ_l1, label='accZ') +plt.legend(loc='lower right') +plt.xlabel('Time') +plt.ylabel('Value') +plt.title('Wavelet Approximation') +plt.grid() +plt.box(False) +plt.show() +``` + +![](images/imgs_dsp_spectral_features/wavelet_input.png){fig-align="center" width="6.5in"} + +### Feature Extraction + +Let's start with the basic statistical features. Note that we apply the function for both the original signals and the resultant cAs from the DWT: + +``` python +def calculate_statistics(signal): + n5 = np.percentile(signal, 5) + n25 = np.percentile(signal, 25) + n75 = np.percentile(signal, 75) + n95 = np.percentile(signal, 95) + median = np.percentile(signal, 50) + mean = np.mean(signal) + std = np.std(signal) + var = np.var(signal) + rms = np.sqrt(np.mean(np.square(signal))) + return [n5, n25, n75, n95, median, mean, std, var, rms] + +stat_feat_l0 = [calculate_statistics(x) for x in sensors] +stat_feat_l1 = [calculate_statistics(x) for x in sensors_l1] +``` + +The Skelness and Kurtosis: + +``` python +skew_l0 = [skew(x, bias=False) for x in sensors] +skew_l1 = [skew(x, bias=False) for x in sensors_l1] +kurtosis_l0 = [kurtosis(x, bias=False) for x in sensors] +kurtosis_l1 = [kurtosis(x, bias=False) for x in sensors_l1] +``` + +**Zero crossing (zcross)** is the number of times the wavelet coefficient crosses the zero axis. It can be used to measure the signal's frequency content since high-frequency signals tend to have more zero crossings than low-frequency signals. + +**Mean crossing (mcross)**, on the other hand, is the number of times the wavelet coefficient crosses the mean of the signal. It can be used to measure the amplitude since high-amplitude signals tend to have more mean crossings than low-amplitude signals. + +``` python +def getZeroCrossingRate(arr): + my_array = np.array(arr) + zcross = float("{0:.2f}".format((((my_array[:-1] * my_array[1:]) < 0).su m())/len(arr))) + return zcross + +def getMeanCrossingRate(arr): + mcross = getZeroCrossingRate(np.array(arr) - np.mean(arr)) + return mcross + +def calculate_crossings(list): + zcross=[] + mcross=[] + for i in range(len(list)): + zcross_i = getZeroCrossingRate(list[i]) + zcross.append(zcross_i) + mcross_i = getMeanCrossingRate(list[i]) + mcross.append(mcross_i) + return zcross, mcross + +cross_l0 = calculate_crossings(sensors) +cross_l1 = calculate_crossings(sensors_l1) +``` + +In wavelet analysis, **entropy** refers to the degree of disorder or randomness in the distribution of wavelet coefficients. Here, we used Shannon entropy, which measures a signal's uncertainty or randomness. It is calculated as the negative sum of the probabilities of the different possible outcomes of the signal multiplied by their base 2 logarithm. In the context of wavelet analysis, Shannon entropy can be used to measure the complexity of the signal, with higher values indicating greater complexity. + +``` python +def calculate_entropy(signal, base=None): + value, counts = np.unique(signal, return_counts=True) + return entropy(counts, base=base) + +entropy_l0 = [calculate_entropy(x) for x in sensors] +entropy_l1 = [calculate_entropy(x) for x in sensors_l1] +``` + +Let's now list all the wavelet features and create a list by layers. + +``` python +L1_features_names = ["L1-n5", "L1-n25", "L1-n75", "L1-n95", "L1-median", "L1-mean", "L1-std", "L1-var", "L1-rms", "L1-skew", "L1-Kurtosis", "L1-zcross", "L1-mcross", "L1-entropy"] + +L0_features_names = ["L0-n5", "L0-n25", "L0-n75", "L0-n95", "L0-median", "L0-mean", "L0-std", "L0-var", "L0-rms", "L0-skew", "L0-Kurtosis", "L0-zcross", "L0-mcross", "L0-entropy"] + +all_feat_l0 = [] +for i in range(len(axis)): + feat_l0 = stat_feat_l0[i]+[skew_l0[i]]+[kurtosis_l0[i]]+[cross_l0[0][i]]+[cross_l0[1][i]]+[entropy_l0[i]] + [print(axis[i]+' '+x+'= ', round(y, 4)) for x,y in zip(L0_features_names, feat_l0)][0] + all_feat_l0.append(feat_l0) +all_feat_l0 = [item for sublist in all_feat_l0 for item in sublist] +print(f"\nAll L0 Features = {len(all_feat_l0)}") + +all_feat_l1 = [] +for i in range(len(axis)): +feat_l1 = stat_feat_l1[i]+[skew_l1[i]]+[kurtosis_l1[i]]+[cross_l1[0][i]]+[cross_l1[1][i]]+[entropy_l1[i]] +[print(axis[i]+' '+x+'= ', round(y, 4)) for x,y in zip(L1_features_names, feat_l1)][0] +all_feat_l1.append(feat_l1) +all_feat_l1 = [item for sublist in all_feat_l1 for item in sublist] +print(f"\nAll L1 Features = {len(all_feat_l1)}") +``` + +![](images/imgs_dsp_spectral_features/wav_result.png){fig-align="center" width="3.58in"} + +## Conclusion + +Edge Impulse Studio is a powerful online platform that can handle the pre-processing task for us. Still, given our engineering perspective, we want to understand what is happening under the hood. This knowledge will help us find the best options and hyper-parameters for tuning our projects. + +Daniel Situnayake wrote in his [blog:](https://situnayake.com/) "Raw sensor data is highly dimensional and noisy. Digital signal processing algorithms help us sift the signal from the noise. DSP is an essential part of embedded engineering, and many edge processors have on-board acceleration for DSP. As an ML engineer, learning basic DSP gives you superpowers for handling high-frequency time series data in your models." 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------------------------------ 1 file changed, 404 deletions(-) delete mode 100644 Motion_Classif_Anomaly_Detect.qmd diff --git a/Motion_Classif_Anomaly_Detect.qmd b/Motion_Classif_Anomaly_Detect.qmd deleted file mode 100644 index bcddb36c..00000000 --- a/Motion_Classif_Anomaly_Detect.qmd +++ /dev/null @@ -1,404 +0,0 @@ -# Motion Classification and Anomaly Detection {.unnumbered} - -## Introduction - -Transportation is the backbone of global commerce. Millions of containers are transported daily via various means, such as ships, trucks, and trains, to destinations worldwide. Ensuring these containers' safe and efficient transit is a monumental task that requires leveraging modern technology, and TinyML is undoubtedly one of them. - -In this hands-on tutorial, we will work to solve real-world problems related to transportation. We will develop a Motion Classification and Anomaly Detection system using the Arduino Nicla Vision board, the Arduino IDE, and the Edge Impulse Studio. This project will help us understand how containers experience different forces and motions during various phases of transportation, such as terrestrial and maritime transit, vertical movement via forklifts, and stationary periods in warehouses. - -### Learning Objectives - -1. Setting up the Arduino Nicla Vision Board -2. Data Collection and Preprocessing -3. Building the Motion Classification Model -4. Implementing Anomaly Detection -5. Real-world Testing and Analysis -6. Conclusion and Next Steps - -By the end of this tutorial, you'll have a working prototype that can classify different types of motion and detect anomalies during the transportation of containers. This knowledge can be a stepping stone to more advanced projects in the burgeoning field of TinyML involving vibration. - -## IMU Installation and testing - -For this project, we will use an accelerometer. As discussed in the Hands-On Tutorial, *Setup Nicla Vision*, the Nicla Vision Board has an onboard **6-axis IMU**: 3D gyroscope and 3D accelerometer, the [LSM6DSOX](https://www.st.com/resource/en/datasheet/lsm6dsox.pdf). Let's verify if the [LSM6DSOX IMU library](https://github.com/arduino-libraries/Arduino_LSM6DSOX) is installed. If not, install it. - -![](images/imgs_motion_classif/imu_ide.jpg){fig-align="center" width="6.5in"} - -Next, go to `Examples > Arduino_LSM6DSOX > SimpleAccelerometer` and run the accelerometer test. You can check if it works by opening the IDE Serial Monitor or Plotter. The values are in g (earth gravity), with a default range of +/- 4g: - -![](images/imgs_motion_classif/imu_test.jpg){fig-align="center" width="6.5in"} - -### Defining the Sampling frequency: - -Choosing an appropriate sampling frequency is crucial for capturing the motion characteristics you're interested in studying. The Nyquist-Shannon sampling theorem states that the sampling rate should be at least twice the highest frequency component in the signal to reconstruct it properly. In the context of motion classification and anomaly detection for transportation, the choice of sampling frequency would depend on several factors: - -1. **Nature of the Motion**: Different types of transportation (terrestrial, maritime, etc.) may involve different ranges of motion frequencies. Faster movements may require higher sampling frequencies. - -2. **Hardware Limitations**: The Arduino Nicla Vision board and any associated sensors may have limitations on how fast they can sample data. - -3. **Computational Resources**: Higher sampling rates will generate more data, which might be computationally intensive, especially critical in a TinyML environment. - -4. **Battery Life**: A higher sampling rate will consume more power. If the system is battery-operated, this is an important consideration. - -5. **Data Storage**: More frequent sampling will require more storage space, another crucial consideration for embedded systems with limited memory. - -In many human activity recognition tasks, **sampling rates of around 50 Hz to 100 Hz** are commonly used. Given that we are simulating transportation scenarios, which are generally not high-frequency events, a sampling rate in that range (50-100 Hz) might be a reasonable starting point. - -Let's define a sketch that will allow us to capture our data with a defined sampling frequency (for example, 50Hz): - -``` cpp -/* - * Based on Edge Impulse Data Forwarder Example (Arduino) - - https://docs.edgeimpulse.com/docs/cli-data-forwarder - * Developed by M.Rovai @11May23 - */ - -/* Include ----------------------------------------------------------------- */ -#include - -/* Constant defines -------------------------------------------------------- */ -#define CONVERT_G_TO_MS2 9.80665f -#define FREQUENCY_HZ 50 -#define INTERVAL_MS (1000 / (FREQUENCY_HZ + 1)) - -static unsigned long last_interval_ms = 0; -float x, y, z; - -void setup() { - Serial.begin(9600); - while (!Serial); - - if (!IMU.begin()) { - Serial.println("Failed to initialize IMU!"); - while (1); - } -} - -void loop() { - if (millis() > last_interval_ms + INTERVAL_MS) { - last_interval_ms = millis(); - - if (IMU.accelerationAvailable()) { - // Read raw acceleration measurements from the device - IMU.readAcceleration(x, y, z); - - // converting to m/s2 - float ax_m_s2 = x * CONVERT_G_TO_MS2; - float ay_m_s2 = y * CONVERT_G_TO_MS2; - float az_m_s2 = z * CONVERT_G_TO_MS2; - - Serial.print(ax_m_s2); - Serial.print("\t"); - Serial.print(ay_m_s2); - Serial.print("\t"); - Serial.println(az_m_s2); - } - } -} -``` - -Uploading the sketch and inspecting the Serial Monitor, we can see that we are capturing 50 samples per second. - -![](images/imgs_motion_classif/sampling.jpg){fig-align="center" width="6.5in"} - -> Note that with the Nicla board resting on a table (with the camera facing down), the z-axis measures around 9.8m/s$^2$, the expected earth acceleration. - -## The Case Study: Simulated Container Transportation - -We will simulate container (or better package) transportation through different scenarios to make this tutorial more relatable and practical. Using the built-in accelerometer of the Arduino Nicla Vision board, we'll capture motion data by manually simulating the conditions of: - -1. **Terrestrial** Transportation (by road or train) -2. **Maritime**-associated Transportation -3. Vertical Movement via Fork-**Lift** -4. Stationary **(Idle**) period in a Warehouse - -![](images/imgs_motion_classif/classes.jpg){fig-align="center" width="6.5in"} - -From the above images, we can define for our simulation that primarily horizontal movements (x or y axis) should be associated with the "Terrestrial class," Vertical movements (z-axis) with the "Lift Class," no activity with the "Idle class," and movement on all three axes to [Maritime class.](https://www.containerhandbuch.de/chb_e/stra/index.html?/chb_e/stra/stra_02_03_03.htm) - -![](images/imgs_motion_classif/classes_mov_def.jpg){fig-align="center" width="6.5in"} - -## Data Collection - -For data collection, we can have several options. In a real case, we can have our device, for example, connected directly to one container, and the data collected on a file (for example .CSV) and stored on an SD card (Via SPI connection) or an offline repo in your computer. Data can also be sent remotely to a nearby repository, such as a mobile phone, using Bluetooth (as done in this project: [Sensor DataLogger](https://www.hackster.io/mjrobot/sensor-datalogger-50e44d)). Once your dataset is collected and stored as a .CSV file, it can be uploaded to the Studio using the [CSV Wizard tool](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/csv-wizard). - -> In this [video](https://youtu.be/2KBPq_826WM), you can learn alternative ways to send data to the Edge Impulse Studio. - -### Connecting the device to Edge Impulse - -We will connect the Nicla directly to the Edge Impulse Studio, which will also be used for data pre-processing, model training, testing, and deployment. For that, you have two options: - -1. Download the latest firmware and connect it directly to the `Data Collection` section. -2. Use the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) tool to capture sensor data from the sensor and send it to the Studio. - -Option 1 is more straightforward, as we saw in the *Setup Nicla Vision* hands-on, but option 2 will give you more flexibility regarding capturing your data, such as sampling frequency definition. Let's do it with the last one. - -Please create a new project on the Edge Impulse Studio (EIS) and connect the Nicla to it, following these steps: - -1. Install the [Edge Impulse CLI](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-installation) and the [Node.js](https://nodejs.org/en/) into your computer. -2. Upload a sketch for data capture (the one discussed previously in this tutorial). -3. Use the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) to capture data from the Nicla's accelerometer and send it to the Studio, as shown in this diagram: - -![](images/imgs_motion_classif/data-forw.jpg){fig-align="center" width="5.25in"} - -Start the [CLI Data Forwarder](https://docs.edgeimpulse.com/docs/edge-impulse-cli/cli-data-forwarder) on your terminal, entering (if it is the first time) the following command: - -``` -$ edge-impulse-data-forwarder --clean -``` - -Next, enter your EI credentials and choose your project, variables (for example, *accX,* *accY*, and *accZ*), and device name (for example, *NiclaV*: - -![](images/imgs_motion_classif/term.jpg){fig-align="center" width="6.5in"} - -Go to the `Devices` section on your EI Project and verify if the device is connected (the dot should be green): - -![](images/imgs_motion_classif/device.jpg){fig-align="center" width="6.5in"} - -> You can clone the project developed for this hands-on: [NICLA Vision Movement Classification](https://studio.edgeimpulse.com/public/302078/latest). - -### Data Collection - -On the `Data Acquisition` section, you should see that your board `[NiclaV]` is connected. The sensor is available: `[sensor with 3 axes (accX, accY, accZ)]` with a sampling frequency of `[50Hz]`. The Studio suggests a sample length of `[10000]` ms (10s). The last thing left is defining the sample label. Let's start with`[terrestrial]`: - -![](images/imgs_motion_classif/collect_data.jpg){fig-align="center" width="5.64in"} - -**Terrestrial** (palettes in a Truck or Train), moving horizontally. Press `[Start Sample]`and move your device horizontally, keeping one direction over your table. After 10 s, your data will be uploaded to the studio. Here is how the sample was collected: - -![](images/imgs_motion_classif/terrestrial_result.jpg){fig-align="center" width="6.5in"} - -As expected, the movement was captured mainly in the Y-axis (green). In the blue, we see the Z axis, around -10 m/s$^2$ (the Nicla has the camera facing up). - -As discussed before, we should capture data from all four Transportation Classes. So, imagine that you have a container with a built-in accelerometer facing the following situations: - -**Maritime** (pallets in boats into an angry ocean). The movement is captured on all three axes: - -![](images/imgs_motion_classif/maritime_result.jpg){fig-align="center" width="6.5in"} - -**Lift** (Palettes being handled vertically by a Forklift). Movement captured only in the Z-axis: - -![](images/imgs_motion_classif/lift_result.jpg){fig-align="center" width="6.5in"} - -**Idle** (Paletts in a warehouse). No movement detected by the accelerometer: - -![](images/imgs_motion_classif/idle_result.jpg){fig-align="center" width="6.5in"} - -You can capture, for example, 2 minutes (twelve samples of 10 seconds) for each of the four classes (a total of 8 minutes of data). Using the `three dots` menu after each one of the samples, select 2 of them, reserving them for the Test set. Alternatively, you can use the automatic `Train/Test Split tool` on the `Danger Zone` of `Dashboard` tab. Below, you can see the resulting dataset: - -![](images/imgs_motion_classif/dataset.jpg){fig-align="center" width="6.5in"} - -Once you have captured your dataset, you can explore it in more detail using the [Data Explorer](https://docs.edgeimpulse.com/docs/edge-impulse-studio/data-acquisition/data-explorer), a visual tool to find outliers or mislabeled data (helping to correct them). The data explorer first tries to extract meaningful features from your data (by applying signal processing and neural network embeddings) and then uses a dimensionality reduction algorithm such as [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) or [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) to map these features to a 2D space. This gives you a one-look overview of your complete dataset. - -![](images/imgs_motion_classif/data_explorer.jpg){fig-align="center" width="6.5in"} - -In our case, the dataset seems OK (good separation). But the PCA shows we can have issues between maritime (green) and lift (orange). This is expected, once on a boat, sometimes the movement can be only "vertical". - -## Impulse Design - -The next step is the definition of our Impulse, which takes the raw data and uses signal processing to extract features, passing them as the input tensor of a *learning block* to classify new data. Go to `Impulse Design` and `Create Impulse`. The Studio will suggest the basic design. Let's also add a second *Learning Block* for `Anomaly Detection`. - -![](images/imgs_motion_classif/impulse.jpg){fig-align="center" width="6.5in"} - -This second model uses a K-means model. If we imagine that we could have our known classes as clusters, any sample that could not fit on that could be an outlier, an anomaly such as a container rolling out of a ship on the ocean or falling from a Forklift. - -![](images/imgs_motion_classif/anomaly_detect.jpg){fig-align="center" width="6.5in"} - -The sampling frequency should be automatically captured, if not, enter it: `[50]`Hz. The Studio suggests a *Window Size* of 2 seconds (`[2000]` ms) with a *sliding window* of `[20]`ms. What we are defining in this step is that we will pre-process the captured data (Time-Seres data), creating a tabular dataset features) that will be the input for a Neural Networks Classifier (DNN) and an Anomaly Detection model (K-Means), as shown below: - -![](images/imgs_motion_classif/impulse-block.jpg){fig-align="center" width="6.5in"} - -Let's dig into those steps and parameters to understand better what we are doing here. - -### Data Pre-Processing Overview - -Data pre-processing is extracting features from the dataset captured with the accelerometer, which involves processing and analyzing the raw data. Accelerometers measure the acceleration of an object along one or more axes (typically three, denoted as X, Y, and Z). These measurements can be used to understand various aspects of the object's motion, such as movement patterns and vibrations. - -Raw accelerometer data can be noisy and contain errors or irrelevant information. Preprocessing steps, such as filtering and normalization, can clean and standardize the data, making it more suitable for feature extraction. In our case, we should divide the data into smaller segments or **windows**. This can help focus on specific events or activities within the dataset, making feature extraction more manageable and meaningful. The **window size** and overlap (**window increase**) choice depend on the application and the frequency of the events of interest. As a thumb rule, we should try to capture a couple of "cycles of data". - -> With a sampling rate (SR) of 50Hz and a window size of 2 seconds, we will get 100 samples per axis, or 300 in total (3 axis x 2 seconds x 50 samples). We will slide this window every 200ms, creating a larger dataset where each instance has 300 raw features. - -![](images/imgs_motion_classif/pre-process.jpg){fig-align="center" width="6.5in"} - -Once the data is preprocessed and segmented, you can extract features that describe the motion's characteristics. Some typical features extracted from accelerometer data include: - -- **Time-domain** features describe the data's statistical properties within each segment, such as mean, median, standard deviation, skewness, kurtosis, and zero-crossing rate. -- **Frequency-domain** features are obtained by transforming the data into the frequency domain using techniques like the Fast Fourier Transform (FFT). Some typical frequency-domain features include the power spectrum, spectral energy, dominant frequencies (amplitude and frequency), and spectral entropy. -- **Time-frequency** domain features combine the time and frequency domain information, such as the Short-Time Fourier Transform (STFT) or the Discrete Wavelet Transform (DWT). They can provide a more detailed understanding of how the signal's frequency content changes over time. - -In many cases, the number of extracted features can be large, which may lead to overfitting or increased computational complexity. Feature selection techniques, such as mutual information, correlation-based methods, or principal component analysis (PCA), can help identify the most relevant features for a given application and reduce the dimensionality of the dataset. The Studio can help with such feature importance calculations. - -### EI Studio Spectral Features - -Data preprocessing is a challenging area for embedded machine learning, still, Edge Impulse helps overcome this with its digital signal processing (DSP) preprocessing step and, more specifically, the [Spectral Features Block](https://docs.edgeimpulse.com/docs/edge-impulse-studio/processing-blocks/spectral-features). - -On the Studio, the collected raw dataset will be the input of a Spectral Analysis block, which is excellent for analyzing repetitive motion, such as data from accelerometers. This block will perform a DSP (Digital Signal Processing), extracting features such as [FFT](https://en.wikipedia.org/wiki/Fast_Fourier_transform) or [Wavelets](https://en.wikipedia.org/wiki/Digital_signal_processing#Wavelet). - -For our project, once the time signal is continuous, we should use FFT with, for example, a length of `[32]`. - -The per axis/channel **Time Domain Statistical features** are: - -- [RMS](https://en.wikipedia.org/wiki/Root_mean_square): 1 feature -- [Skewness](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FSkewness): 1 feature -- [Kurtosis](https://colab.research.google.com/corgiredirector?site=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FKurtosis): 1 feature - -The per axis/channel **Frequency Domain Spectral features** are: - -- [Spectral Power](https://en.wikipedia.org/wiki/Spectral_density): 16 features (FFT Length/2) -- Skewness: 1 feature -- Kurtosis: 1 feature - -So, for an FFT length of 32 points, the resulting output of the Spectral Analysis Block will be 21 features per axis (a total of 63 features). - -> You can learn more about how each feature is calculated by downloading the notebook [Edge Impulse - Spectral Features Block Analysis](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Motion_Classification/Edge_Impulse_Spectral_Features_Block.ipynb) [TinyML under the hood: Spectral Analysis](https://www.hackster.io/mjrobot/tinyml-under-the-hood-spectral-analysis-94676c) or [opening it directly on Google CoLab](https://colab.research.google.com/github/Mjrovai/Arduino_Nicla_Vision/blob/main/Motion_Classification/Edge_Impulse_Spectral_Features_Block.ipynb). - -### Generating features - -Once we understand what the pre-processing does, it is time to finish the job. So, let's take the raw data (time-series type) and convert it to tabular data. For that, go to the `Spectral Features` section on the `Parameters` tab, define the main parameters as discussed in the previous section (`[FFT]` with `[32]` points), and select`[Save Parameters]`: - -![](images/imgs_motion_classif/Parameters_definition.jpg){fig-align="center" width="6.5in"} - -At the top menu, select the `Generate Features` option and the `Generate Features` button. Each 2-second window data will be converted into one data point of 63 features. - -> The Feature Explorer will show those data in 2D using [UMAP.](https://umap-learn.readthedocs.io/en/latest/) Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE but is also applicable for general non-linear dimension reduction. - -The visualization makes it possible to verify that after the feature generation, the classes present keep their excellent separation, which indicates that the classifier should work well. Optionally, you can analyze how important each one of the features is for one class compared with others. - -![](images/imgs_motion_classif/feature_generation.jpg){fig-align="center" width="6.5in"} - -## Models Training - -Our classifier will be a Dense Neural Network (DNN) that will have 63 neurons on its input layer, two hidden layers with 20 and 10 neurons, and an output layer with four neurons (one per each class), as shown here: - -![](images/imgs_motion_classif/model.jpg){fig-align="center" width="6.5in"} - -As hyperparameters, we will use a Learning Rate of `[0.005]`, a Batch size of `[32]`, and `[20]`% of data for validation for `[30]` epochs. After training, we can see that the accuracy is 98.5%. The cost of memory and latency is meager. - -![](images/imgs_motion_classif/train.jpg){fig-align="center" width="6.5in"} - -For Anomaly Detection, we will choose the suggested features that are precisely the most important ones in the Feature Extraction, plus the accZ RMS. The number of clusters will be `[32]`, as suggested by the Studio: - -![](images/imgs_motion_classif/anom_det_train.jpg){fig-align="center" width="6.5in"} - -## Testing - -We can verify how our model will behave with unknown data using 20% of the data left behind during the data capture phase. The result was almost 95%, which is good. You can always work to improve the results, for example, to understand what went wrong with one of the wrong results. If it is a unique situation, you can add it to the training dataset and then repeat it. - -The default minimum threshold for a considered uncertain result is `[0.6]` for classification and `[0.3]` for anomaly. Once we have four classes (their output sum should be 1.0), you can also set up a lower threshold for a class to be considered valid (for example, 0.4). You can `Set confidence thresholds` on the `three dots` menu, besides the `Classy all` button. - -![](images/imgs_motion_classif/model_testing.jpg){fig-align="center" width="6.5in"} - -You can also perform Live Classification with your device (which should still be connected to the Studio). - -> Be aware that here, you will capture real data with your device and upload it to the Studio, where an inference will be taken using the trained model (But the **model is NOT in your device**). - -## Deploy - -It is time to deploy the preprocessing block and the trained model to the Nicla. The Studio will package all the needed libraries, preprocessing functions, and trained models, downloading them to your computer. You should select the option `Arduino Library`, and at the bottom, you can choose `Quantized (Int8)` or `Unoptimized (float32)` and `[Build]`. A Zip file will be created and downloaded to your computer. - -![](images/imgs_motion_classif/deploy.jpg){fig-align="center" width="6.5in"} - -On your Arduino IDE, go to the `Sketch` tab, select `Add.ZIP Library`, and Choose the.zip file downloaded by the Studio. A message will appear in the IDE Terminal: `Library installed`. - -### Inference - -Now, it is time for a real test. We will make inferences wholly disconnected from the Studio. Let's change one of the code examples created when you deploy the Arduino Library. - -In your Arduino IDE, go to the `File/Examples` tab and look for your project, and on examples, select `Nicla_vision_fusion`: - -![](images/imgs_motion_classif/inference.jpg){fig-align="center" width="6.5in"} - -Note that the code created by Edge Impulse considers a *sensor fusion* approach where the IMU (Accelerometer and Gyroscope) and the ToF are used. At the beginning of the code, you have the libraries related to our project, IMU and ToF: - -``` cpp -/* Includes ---------------------------------------------------------------- */ -#include -#include //IMU -#include "VL53L1X.h" // ToF -``` - -> You can keep the code this way for testing because the trained model will use only features pre-processed from the accelerometer. But consider that you will write your code only with the needed libraries for a real project. - -And that is it! - -You can now upload the code to your device and proceed with the inferences. Press the Nicla `[RESET]` button twice to put it on boot mode (disconnect from the Studio if it is still connected), and upload the sketch to your board. - -Now you should try different movements with your board (similar to those done during data capture), observing the inference result of each class on the Serial Monitor: - -- **Idle and lift classes:** - -![](images/imgs_motion_classif/inference_1.jpg){fig-align="center" width="6.5in"} - -- **maritime and terrestrial**: - -![](images/imgs_motion_classif/inference_2.jpg){fig-align="center" width="6.5in"} - -Note that in all situations above, the value of the `anomaly score` was smaller than 0.0. Try a new movement that was not part of the original dataset, for example, "rolling" the Nicla, facing the camera upside-down, as a container falling from a boat or even a boat accident: - -- **anomaly detection**: - -![](images/imgs_motion_classif/anomaly-boat.jpg){fig-align="center" width="6.5in"} - -In this case, the anomaly is much bigger, over 1.00 - -### Post-processing - -Now that we know the model is working since it detects the movements, we suggest that you modify the code to see the result with the NiclaV completely offline (disconnected from the PC and powered by a battery, a power bank, or an independent 5V power supply). - -The idea is to do the same as with the KWS project: if one specific movement is detected, a specific LED could be lit. For example, if *terrestrial* is detected, the Green LED will light; if *maritime*, the Red LED will light, if it is a *lift,* the Blue LED will light; and if no movement is detected *(idle*), the LEDs will be OFF. You can also add a condition when an anomaly is detected, in this case, for example, a white color can be used (all e LEDs light simultaneously). - -## Conclusion - -> The notebooks and codes used in this hands-on tutorial will be found on the [GitHub](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/Motion_Classification) repository. - -Before we finish, consider that Movement Classification and Object Detection can be utilized in many applications across various domains. Here are some of the potential applications: - -### Case Applications - -#### Industrial and Manufacturing - -- **Predictive Maintenance**: Detecting anomalies in machinery motion to predict failures before they occur. -- **Quality Control**: Monitoring the motion of assembly lines or robotic arms for precision assessment and deviation detection from the standard motion pattern. -- **Warehouse Logistics**: Managing and tracking the movement of goods with automated systems that classify different types of motion and detect anomalies in handling. - -#### Healthcare - -- **Patient Monitoring**: Detecting falls or abnormal movements in the elderly or those with mobility issues. -- **Rehabilitation**: Monitoring the progress of patients recovering from injuries by classifying motion patterns during physical therapy sessions. -- **Activity Recognition**: Classifying types of physical activity for fitness applications or patient monitoring. - -#### Consumer Electronics - -- **Gesture Control**: Interpreting specific motions to control devices, such as turning on lights with a hand wave. -- **Gaming**: Enhancing gaming experiences with motion-controlled inputs. - -#### Transportation and Logistics - -- **Vehicle Telematics**: Monitoring vehicle motion for unusual behavior such as hard braking, sharp turns, or accidents. -- **Cargo Monitoring**: Ensuring the integrity of goods during transport by detecting unusual movements that could indicate tampering or mishandling. - -#### Smart Cities and Infrastructure - -- **Structural Health Monitoring**: Detecting vibrations or movements within structures that could indicate potential failures or maintenance needs. -- **Traffic Management**: Analyzing the flow of pedestrians or vehicles to improve urban mobility and safety. - -#### Security and Surveillance - -- **Intruder Detection**: Detecting motion patterns typical of unauthorized access or other security breaches. -- **Wildlife Monitoring**: Detecting poachers or abnormal animal movements in protected areas. - -#### Agriculture - -- **Equipment Monitoring**: Tracking the performance and usage of agricultural machinery. -- **Animal Behavior Analysis**: Monitoring livestock movements to detect behaviors indicating health issues or stress. - -#### Environmental Monitoring - -- **Seismic Activity**: Detecting irregular motion patterns that precede earthquakes or other geologically relevant events. -- **Oceanography**: Studying wave patterns or marine movements for research and safety purposes. - -### Nicla 3D case - -For real applications, as some described before, we can add a case to our device, and Eoin Jordan, from Edge Impulse, developed a great wearable and machine health case for the Nicla range of boards. It works with a 10mm magnet, 2M screws, and a 16mm strap for human and machine health use case scenarios. Here is the link: [Arduino Nicla Voice and Vision Wearable Case](https://www.thingiverse.com/thing:5923305). - -![](images/imgs_motion_classif/case.jpg){fig-align="center" width="6.5in"} - -The applications for motion classification and anomaly detection are extensive, and the Arduino Nicla Vision is well-suited for scenarios where low power consumption and edge processing are advantageous. Its small form factor and efficiency in processing make it an ideal choice for deploying portable and remote applications where real-time processing is crucial and connectivity may be limited. From e712177c90aab5c329645700b699ca713c949273 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Tue, 7 Nov 2023 14:06:46 +0000 Subject: [PATCH 20/59] Update readme and contributors.qmd with contributors --- .all-contributorsrc | 140 ++++++++++++++++++++++---------------------- README.md | 30 +++++----- contributors.qmd | 30 +++++----- 3 files changed, 100 insertions(+), 100 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 1dd4e3b4..00d31050 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -7,37 +7,46 @@ ], "contributors": [ { - "login": "BaeHenryS", - "name": "Henry Bae", - "avatar_url": "https://avatars.githubusercontent.com/BaeHenryS", - "profile": "https://github.com/BaeHenryS", + "login": "mpstewart1", + "name": "Matthew Stewart", + "avatar_url": "https://avatars.githubusercontent.com/mpstewart1", + "profile": "https://github.com/mpstewart1", "contributions": [ "doc" ] }, { - "login": "oishib", - "name": "oishib", - "avatar_url": "https://avatars.githubusercontent.com/oishib", - 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"login": "aptl26", - "name": "aptl26", - "avatar_url": "https://avatars.githubusercontent.com/aptl26", - "profile": "https://github.com/aptl26", + "login": "colbybanbury", + "name": "Colby Banbury", + "avatar_url": "https://avatars.githubusercontent.com/colbybanbury", + "profile": "https://github.com/colbybanbury", "contributions": [ "doc" ] @@ -70,19 +79,28 @@ ] }, { - "login": "marcozennaro", - "name": "Marco Zennaro", - "avatar_url": "https://avatars.githubusercontent.com/marcozennaro", - "profile": "https://github.com/marcozennaro", + "login": "BaeHenryS", + "name": "Henry Bae", + "avatar_url": "https://avatars.githubusercontent.com/BaeHenryS", + "profile": "https://github.com/BaeHenryS", "contributions": [ "doc" ] }, { - "login": "jaysonzlin", - "name": "Jayson Lin", - "avatar_url": "https://avatars.githubusercontent.com/jaysonzlin", - "profile": "https://github.com/jaysonzlin", + "login": "uchendui", + "name": "Ikechukwu Uchendu", + "avatar_url": "https://avatars.githubusercontent.com/uchendui", + "profile": "https://github.com/uchendui", + "contributions": [ + "doc" + ] + }, + { + "login": "jessicaquaye", + "name": "Jessica Quaye", + "avatar_url": "https://avatars.githubusercontent.com/jessicaquaye", + "profile": "https://github.com/jessicaquaye", "contributions": [ "doc" ] @@ -97,19 +115,19 @@ ] }, { - 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"login": "colbybanbury", - "name": "Colby Banbury", - "avatar_url": "https://avatars.githubusercontent.com/colbybanbury", - "profile": "https://github.com/colbybanbury", + "login": "mmaz", + "name": "Mark Mazumder", + "avatar_url": "https://avatars.githubusercontent.com/mmaz", + "profile": "https://github.com/mmaz", "contributions": [ "doc" ] diff --git a/README.md b/README.md index a6b4deda..2f59b15b 100644 --- a/README.md +++ b/README.md @@ -88,32 +88,32 @@ quarto render - - + - - + + + - + + - - - + + - - - + + + - - + + - - + +
      Henry Bae
      Henry Bae

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      Vijay Janapa Reddi

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      diff --git a/contributors.qmd b/contributors.qmd index c2f56231..53c3c80f 100644 --- a/contributors.qmd +++ b/contributors.qmd @@ -8,32 +8,32 @@ We extend our sincere thanks to the diverse group of individuals who have genero - - + - - + + + - + + - - - + + - - - + + + - - + + - - + +
      Henry Bae
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