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Course Outline
Introduction to Federated Learning
- Overview of Federated Learning concepts
- Decentralized model training vs. traditional centralized approaches
- Benefits of Federated Learning in privacy and data security
Basic Federated Learning Algorithms
- Introduction to Federated Averaging
- Implementation of a simple Federated Learning model
- Comparison of Federated Learning with traditional machine learning
Data Privacy and Security in Federated Learning
- Understanding data privacy concerns in AI
- Techniques for enhancing privacy in Federated Learning
- Secure aggregation and data encryption methods
Practical Implementation of Federated Learning
- Setting up a Federated Learning environment
- Building and training a Federated Learning model
- Deploying Federated Learning in real-world scenarios
Challenges and Limitations of Federated Learning
- Handling non-IID data in Federated Learning
- Communication and synchronization issues
- Scaling Federated Learning for large networks
Case Studies and Future Trends
- Case studies of successful Federated Learning implementations
- Exploring the future of Federated Learning
- Emerging trends in privacy-preserving AI
Summary and Next Steps
Requirements
- Basic understanding of machine learning concepts
- Experience with Python programming
- Familiarity with data privacy principles
Audience
- Data scientists
- Machine learning enthusiasts
- AI beginners
14 Hours
