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Course Outline
Introduction to Federated Learning
- Key concepts in Federated Learning
- Decentralized model training compared to traditional centralized methods
- Advantages of Federated Learning for privacy and data security
Foundational Federated Learning Algorithms
- Overview of Federated Averaging
- Building a simple Federated Learning model
- Contrasting Federated Learning with conventional machine learning
Data Privacy and Security in Federated Learning
- Understanding privacy challenges in AI
- Methods to strengthen privacy within Federated Learning
- Techniques for secure aggregation and data encryption
Practical Application of Federated Learning
- Configuring a Federated Learning environment
- Developing and training a Federated Learning model
- Implementing Federated Learning in real-world contexts
Challenges and Limitations of Federated Learning
- Managing non-IID data in Federated Learning
- Addressing communication and synchronization hurdles
- Scaling Federated Learning for extensive networks
Case Studies and Future Directions
- Examples of successful Federated Learning deployments
- The evolving landscape of Federated Learning
- Emerging trends in privacy-preserving AI
Summary and Next Steps
Requirements
- A fundamental grasp of machine learning concepts
- Proficiency in Python programming
- Knowledge of data privacy principles
Target Audience
- Data scientists
- Machine learning enthusiasts
- Beginners in AI
14 Hours
