Build sophisticated federated learning systems. This course covers advanced aggregation algorithms, differential privacy, secure multi-party computation, handling data heterogeneity, communication optimization, and system implementation strategies for privacy-preserving machine learning at scale.
Prerequisites ML & Federated Learning Basics
Level:
Advanced Aggregation
Implement and analyze aggregation algorithms beyond FedAvg, addressing issues like heterogeneity and Byzantine clients.
Privacy Enhancement
Apply differential privacy, secure multi-party computation, and homomorphic encryption techniques within federated learning.
Heterogeneity Management
Develop and evaluate strategies for handling statistical (Non-IID data) and systems heterogeneity across clients.
Personalization Techniques
Implement personalization methods in FL, including meta-learning and clustered approaches.
Communication Optimization
Apply gradient compression, sparsification, and quantization techniques to improve communication efficiency.
System Design
Design, implement, and analyze federated learning systems using established frameworks and considering security implications.