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コース概要
Review of Core Federated Learning Concepts
- Recap of basic Federated Learning methodologies
- Challenges in Federated Learning: communication, computation, and privacy
- Introduction to advanced Federated Learning techniques
Optimization Algorithms for Federated Learning
- Overview of optimization challenges in Federated Learning
- Advanced optimization algorithms: Federated Averaging (FedAvg), Federated SGD, and more
- Implementing and tuning optimization algorithms for large-scale federated systems
Handling Non-IID Data in Federated Learning
- Understanding non-IID data and its impact on Federated Learning
- Strategies for handling non-IID data distributions
- Case studies and real-world applications
Scaling Federated Learning Systems
- Challenges in scaling Federated Learning systems
- Techniques for scaling up: architecture design, communication protocols, and more
- Deploying large-scale Federated Learning applications
Advanced Privacy and Security Considerations
- Privacy-preserving techniques in advanced Federated Learning
- Secure aggregation and differential privacy
- Ethical considerations in large-scale Federated Learning
Case Studies and Practical Applications
- Case study: Large-scale Federated Learning in healthcare
- Hands-on practice with advanced Federated Learning scenarios
- Real-world project implementation
Future Trends in Federated Learning
- Emerging research directions in Federated Learning
- Technological advancements and their impact on Federated Learning
- Exploring future opportunities and challenges
Summary and Next Steps
要求
- Experience with machine learning and deep learning techniques
- Understanding of basic Federated Learning concepts
- Proficiency in Python programming
Audience
- Experienced AI researchers
- Machine learning engineers
- Data scientists
21 時間
