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Course Outline
Introduction to Federated Learning
- Overview of key Federated Learning concepts
- Decentralized model training compared to traditional centralized methods
- Advantages of Federated Learning for privacy and data security
Fundamental Federated Learning Algorithms
- Introduction to Federated Averaging
- Building a simple Federated Learning model
- Evaluating Federated Learning against traditional machine learning
Data Privacy and Security in Federated Learning
- Addressing data privacy issues in AI
- Methods to strengthen privacy in Federated Learning
- Secure aggregation and encryption techniques
Practical Application of Federated Learning
- Configuring a Federated Learning environment
- Constructing and training a Federated Learning model
- Deploying Federated Learning in real-world contexts
Challenges and Limitations of Federated Learning
- Managing non-IID data in Federated Learning
- Resolving communication and synchronization hurdles
- Scaling Federated Learning for extensive networks
Case Studies and Future Trends
- Examples of successful Federated Learning deployments
- Examining the future landscape of Federated Learning
- New developments in privacy-preserving AI
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts
- Proficiency in Python programming
- Familiarity with data privacy guidelines
Target Audience
- Data scientists
- Machine learning practitioners
- Beginners in AI
14 Hours
