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コース概要
Introduction to Federated Learning
- What is federated learning, and how does it differ from centralized learning?
- Advantages of federated learning for secure AI collaboration
- Use cases and applications in sensitive data sectors
Core Components of Federated Learning
- Federated data, clients, and model aggregation
- Communication protocols and updates
- Handling heterogeneity in federated environments
Data Privacy and Security in Federated Learning
- Data minimization and privacy principles
- Techniques for securing model updates (e.g., differential privacy)
- Federated learning in compliance with data protection regulations
Implementing Federated Learning
- Setting up a federated learning environment
- Distributed model training with federated frameworks
- Performance and accuracy considerations
Federated Learning in Healthcare
- Secure data sharing and privacy concerns in healthcare
- Collaborative AI for medical research and diagnosis
- Case studies: federated learning in medical imaging and diagnosis
Federated Learning in Finance
- Using federated learning for secure financial modeling
- Fraud detection and risk analysis with federated approaches
- Case studies in secure data collaboration within financial institutions
Challenges and Future of Federated Learning
- Technical and operational challenges in federated learning
- Future trends and advancements in federated AI
- Exploring opportunities for federated learning across industries
Summary and Next Steps
要求
- Basic understanding of machine learning concepts
- Familiarity with data privacy and security fundamentals
Audience
- Data scientists and AI researchers focused on privacy-preserving machine learning
- Healthcare and finance professionals handling sensitive data
- IT and compliance managers interested in secure AI collaboration methods
14 時間
