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Course Outline
Introduction to Federated Learning
- Overview of key Federated Learning concepts
- Comparison of decentralized model training against traditional centralized methods
- Advantages of Federated Learning regarding privacy and data security
Foundational Federated Learning Algorithms
- Introduction to Federated Averaging
- Construction of a basic Federated Learning model
- Contrasting Federated Learning with conventional machine learning approaches
Data Privacy and Security in Federated Learning
- Understanding data privacy challenges in AI
- Strategies for enhancing privacy within Federated Learning
- Secure aggregation and data encryption techniques
Practical Application of Federated Learning
- Configuring a Federated Learning environment
- Developing 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
- Review of successful Federated Learning implementations
- Examining the future trajectory of Federated Learning
- Exploring emerging trends in privacy-preserving AI
Summary and Next Steps
Requirements
- Foundational knowledge of machine learning concepts
- Proficiency in Python programming
- Familiarity with data privacy standards
Target Audience
- Data scientists
- Machine learning enthusiasts
- AI novices
14 Hours
