A list of resources releated to federated learning and privacy in machine learning.
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Advances and Open Problems in Federated Learning https://arxiv.org/pdf/1912.04977.pdf
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Federated Machine Learning: Concept and Applications https://arxiv.org/pdf/1902.04885
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Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection https://arxiv.org/abs/1907.09693
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Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis https://arxiv.org/abs/1802.09941
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EdgeAI: A Visionfor Deep Learning in IoT Era https://arxiv.org/abs/1910.10356
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Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data https://arxiv.org/abs/1910.08663
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No Peek: A Survey of private distributed deep learning https://arxiv.org/pdf/1812.03288
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Federated Learning in Mobile Edge Networks: A Comprehensive Survey https://arxiv.org/abs/1909.11875
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Communication-Efficient Learning of Deep Networks from Decentralized Data https://arxiv.org/pdf/1602.05629.pdf
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Practical Secure Aggregation for Federated Learning on User-Held Data https://arxiv.org/abs/1611.04482
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Practical Secure Aggregation for Privacy-Preserving Machine Learning https://storage.googleapis.com/pub-tools-public-publication-data/pdf/ae87385258d90b9e48377ed49d83d467b45d5776.pdf
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A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/abs/1812.03224
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Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/pdf/1811.12470
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How To Backdoor Federated Learning https://arxiv.org/abs/1807.00459
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Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attack https://arxiv.org/abs/1812.00910
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Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535
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Exploiting Unintended Feature Leakage in Collaborative Learning https://arxiv.org/abs/1805.04049
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Analyzing Federated Learning through an Adversarial Lens https://arxiv.org/abs/1811.12470
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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning https://arxiv.org/abs/1702.07464
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Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984
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Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing https://arxiv.org/abs/1907.10218
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Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274
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Differentially Private Federated Learning: A Client Level Perspective https://arxiv.org/abs/1712.07557
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Privacy-Preserving Collaborative Deep Learning with Unreliable Participants https://arxiv.org/abs/1812.10113
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Scalable Private Learning with PATE https://arxiv.org/abs/1802.08908
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Reducing leakage in distributed deep learning for sensitive health data https://www.media.mit.edu/publications/reducing-leakage-in-distributed-deep-learning-for-sensitive-health-data-accepted-to-iclr-2019-workshop-on-ai-for-social-good-2019/
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Deep Leakage from Gradients http://papers.nips.cc/paper/9617-deep-leakage-from-gradients.pdf
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Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning https://arxiv.org/abs/1805.05838
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Flower: A Friendly Federated Learning Framework https://arxiv.org/pdf/2007.14390.pdf
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Learning Private Neural Language Modeling with Attentive Aggregation https://arxiv.org/pdf/1812.07108
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Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning https://arxiv.org/abs/2003.09603
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Decentralized Knowledge Acquisition for Mobile Internet Applications https://link.springer.com/article/10.1007/s11280-019-00775-w
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A generic framework for privacy preserving deep learning https://arxiv.org/pdf/1811.04017.pdf
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Federated Learning of N-gram Language Models https://arxiv.org/pdf/1910.03432.pdf
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Towards Federated Learning at Scale: System Design https://arxiv.org/pdf/1902.01046.pdf
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Federated Learning for Keyword Spotting https://arxiv.org/abs/1810.05512
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Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data https://arxiv.org/abs/1810.08553
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Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System https://arxiv.org/pdf/1901.09888
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Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence https://arxiv.org/abs/1910.02109
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Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platform http://www.cs.ucf.edu/~mohaisen/doc/dsn19b.pdf
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Institutionally Distributed Deep Learning Networks https://arxiv.org/abs/1709.05929
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Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation https://arxiv.org/abs/1810.04304
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Split learning for health: Distributed deep learning without sharing raw patient data https://www.media.mit.edu/publications/split-learning-for-health-distributed-deep-learning-without-sharing-raw-patient-data/
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Continuous Delivery for Machine Learning https://martinfowler.com/articles/cd4ml.html#EvolvingIntelligentSystemsWithoutBias
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Ease.ml/ci & Ease.ml/meter Towards Data Management for Statistical Generialization http://ease.ml/
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VisionAir: Using Federated Learning to estimate Air Quality using the Tensorflow API for Java https://blog.tensorflow.org/2020/02/visionair-using-federated-learning-to-estimate-airquality-tensorflow-api-java.html
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Federated Optimization in Heterogeneous Networks https://arxiv.org/abs/1812.06127
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Federated Visual Classification with Real-World Data Distribution https://arxiv.org/abs/2003.08082
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Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification https://arxiv.org/abs/1909.06335
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LEAF: A Benchmark for Federated Settings https://arxiv.org/abs/1812.01097
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On the Convergence of FedAvg on Non-IID Data https://arxiv.org/abs/1907.02189
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Privacy-preserving Federated Brain Tumour Segmentation. https://arxiv.org/pdf/1910.00962.pdf
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ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries https://www.media.mit.edu/publications/ExpertMatcher/
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Detailed comparison of communication efficiency of split learning and federated learning https://www.media.mit.edu/publications/detailed-comparison-of-communication-efficiency-of-split-learning-and-federated-learning-1/
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Split Learning: Distributed and collaborative learning https://aiforsocialgood.github.io/iclr2019/accepted/track1/pdfs/31_aisg_iclr2019.pdf
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Asynchronous Federated Optimization https://arxiv.org/pdf/1903.03934
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Robust and Communication-Efficient Federated Learning from Non-IID Data https://arxiv.org/pdf/1903.02891
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One-Shot Federated Learning https://arxiv.org/pdf/1902.11175
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High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions https://arxiv.org/pdf/1902.08999
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Agnostic Federated Learning https://arxiv.org/pdf/1902.00146%C2%A0
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Peer-to-peer Federated Learning on Graphs https://arxiv.org/pdf/1901.11173
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SecureBoost: A Lossless Federated Learning Framework https://arxiv.org/pdf/1901.08755
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Federated Reinforcement Learning https://arxiv.org/pdf/1901.08277
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Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems https://arxiv.org/pdf/1901.06455
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Federated Learning via Over-the-Air Computation https://arxiv.org/pdf/1812.11750
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Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version) https://arxiv.org/pdf/1812.11494
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Multi-objective Evolutionary Federated Learning https://arxiv.org/pdf/1812.07478
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Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach https://arxiv.org/pdf/1812.03633
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A Hybrid Approach to Privacy-Preserving Federated Learning https://arxiv.org/pdf/1812.03224
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Applied Federated Learning: Improving Google Keyboard Query Suggestions https://arxiv.org/pdf/1812.02903
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Differentially Private Data Generative Models https://arxiv.org/pdf/1812.02274
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Protection Against Reconstruction and Its Applications in Private Federated Learning https://arxiv.org/pdf/1812.00984
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Split learning for health: Distributed deep learning without sharing raw patient data https://arxiv.org/pdf/1812.00564
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Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning https://arxiv.org/pdf/1812.00535
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LoAdaBoost:Loss-Based AdaBoost Federated Machine Learning on medical Data https://arxiv.org/pdf/1811.12629
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Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data https://arxiv.org/pdf/1811.11479
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Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning https://arxiv.org/pdf/1811.09904
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Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting https://arxiv.org/pdf/1811.09712
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Federated Learning Approach for Mobile Packet Classification https://arxiv.org/abs/1907.13113
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Collaborative Learning on the Edges: A Case Study on Connected Vehicles https://www.usenix.org/conference/hotedge19/presentation/lu
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Federated Learning for Time Series Forecasting Using Hybrid Model http://www.diva-portal.se/smash/get/diva2:1334629/FULLTEXT01.pdf
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Federated Learning: Challenges, Methods, and Future Directions https://arxiv.org/pdf/1908.07873.pdf
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Federated Learning with Matched Averaging https://openreview.net/forum?id=BkluqlSFDS
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Flower https://flower.dev/
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Tensorflow Federated https://www.tensorflow.org/federated
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DVC https://dvc.org/
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Federated iNaturalist/Landmarkds https://github.com/google-research/google-research/tree/master/federated_vision_datasets
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FedML: A Research Library and Benchmark for Federated Machine Learning https://github.com/FedML-AI/FedML
MIT CSAIL/Harvard Medical/Tsinghua University’s Academy of Arts and Design
- https://arxiv.org/ftp/arxiv/papers/1903/1903.09296.pdf
- https://venturebeat.com/2019/03/25/federated-learning-technique-predicts-hospital-stay-and-patient-mortality/
Microsoft research/University of Chinese Academy of Sciences, Beijing, China
Boston University/Massachusetts General Hospital
- https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
- https://www.statnews.com/2019/09/10/google-mayo-clinic-partnership-patient-data/
Tencent WeBank
Nvidia/King’s College London, American College of Radiology, MGH and BWH Center for Clinical Data Science, and UCLA Health... etc
- https://venturebeat.com/2019/10/13/nvidia-uses-federated-learning-to-create-medical-imaging-ai/
- https://blogs.nvidia.com/blog/2019/12/01/clara-federated-learning/
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Adap https://adap.com/en
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Snips
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Privacy.ai https://privacy.ai/
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OpenMined https://www.openmined.org/
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Arkhn https://arkhn.org/en/
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Scaleout https://scaleoutsystems.com/
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MELLODDY https://www.melloddy.eu/
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DataFleets https://www.datafleets.com/