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Duration 14 hours
Course Outline
Introduction to Federated Learning
- Fundamentals and overview of Federated Learning concepts.
- Contrasting decentralized model training with traditional centralized methods.
- The impact of Federated Learning on privacy and data security.
Core Federated Learning Algorithms
- An introduction to Federated Averaging.
- Building a simple Federated Learning model from scratch.
- Analyzing differences between Federated Learning and conventional machine learning.
Privacy and Security in Federated Learning
- Navigating data privacy challenges within AI systems.
- Strategies to strengthen privacy protocols in Federated Learning.
- Examining secure aggregation and data encryption techniques.
Hands-on Implementation
- Configuring a dedicated Federated Learning environment.
- Constructing and training a functional Federated Learning model.
- Deploying Federated Learning solutions in practical, real-world contexts.
Addressing Challenges and Limitations
- Managing non-IID data distributions in Federated Learning.
- Resolving communication and synchronization bottlenecks.
- Strategies for scaling Federated Learning across large-scale networks.
Case Studies and Future Directions
- Reviewing successful real-world Federated Learning implementations.
- Looking ahead to the evolution of Federated Learning.
- Identifying emerging trends in privacy-preserving AI.
Conclusion and Path Forward
Requirements
- A foundational grasp of machine learning concepts.
- Proficiency in Python programming.
- Awareness of key data privacy principles.
Who Should Attend
- Data scientists seeking new methodologies.
- Machine learning enthusiasts.
- Beginners in AI looking to expand their skill set.
