# On-Device Learning

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## Learning Objectives

* coming soon.

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## 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