Update ondevice_learning.qmd

ondevice_learning.qmd

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 # On-Device Learning
 
-<!--
 
-	Federated learning
-	On-device training
-	Continuous Learning
-	Forward vs. backward pass methods
+## Introduction
 
--->
+Explanation: This section sets the stage for the reader, explaining why on-device learning is a critical aspect of embedded AI systems.
+
+  - Importance in Embedded AI
+  - Why is On-device Learning Needed
+
+## 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.
+
+  - Benefits
+  - Constraints
+
+## Continuous Learning
+
+Explanation: Continuous learning is essential for embedded systems to adapt to new data and situations without requiring frequent updates from a central server.
+
+  - Incremental Algorithms
+  - Adaptability
+
+## Federated Machine Learning
+
+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.
+
+  - 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.
+
+  - Use Cases
+  - Benefits
+
+## Data Augmentation
+
+Explanation: Data augmentation can enrich the training set, improving model performance, which is particularly useful when data is limited in embedded systems.
+
+  - Techniques
+  - Role in On-Device Learning
+
+## Security Concerns
+
+Explanation: Security is a significant concern for any system that performs learning on-device, as it may expose vulnerabilities.
+
+  - Risks
+  - Mitigation
+
+## Conclusion
+
+  - Key Takeaways