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Course Outline
Introduction to Federated Learning
- Overview of key Federated Learning concepts
- Decentralized model training compared to traditional centralized approaches
- Advantages of Federated Learning regarding privacy and data security
Foundational Federated Learning Algorithms
- Introduction to Federated Averaging
- Building a simple Federated Learning model
- Comparing Federated Learning with traditional machine learning
Data Privacy and Security in Federated Learning
- Examining data privacy challenges in AI
- Techniques to enhance privacy within Federated Learning
- Secure aggregation and data encryption methods
Practical Implementation of Federated Learning
- Setting up a Federated Learning environment
- Developing and training a Federated Learning model
- Deploying Federated Learning in real-world scenarios
Challenges and Limitations of Federated Learning
- Managing non-IID data in Federated Learning
- Addressing communication and synchronization issues
- Scaling Federated Learning for extensive networks
Case Studies and Future Trends
- Case studies showcasing successful Federated Learning implementations
- Exploring the future trajectory of Federated Learning
- Emerging trends in privacy-preserving AI
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning concepts
- Proficiency in Python programming
- Understanding of data privacy principles
Target Audience
- Data scientists
- Machine learning enthusiasts
- Individuals new to AI
14 Hours
