Advanced Federated Learning Techniques
Chapter 1: Federated Learning Foundations Revisited
Federated Learning Principles: A Recap
Challenges in Federated Environments
Mathematical Formulation of Federated Optimization
Synchronous vs. Asynchronous Federated Learning Models
Threat Models in Federated Learning
Evaluating Federated Learning Systems
Chapter 2: Advanced Aggregation Algorithms
Limitations of Federated Averaging (FedAvg)
FedProx: Addressing Statistical Heterogeneity
SCAFFOLD: Variance Reduction in Federated Optimization
FedNova: Normalized Averaging for Heterogeneous Systems
Byzantine-Robust Aggregation Methods
Adaptive Federated Optimization Techniques
Practice: Implementing Advanced Aggregation
Chapter 3: Enhancing Privacy in Federated Learning
Differential Privacy Mechanisms for FL
Applying DP to Gradient Updates
Composition Theorems and Privacy Budget Management
Secure Multi-Party Computation (SMC) Protocols for Aggregation
Homomorphic Encryption (HE) for Secure Aggregation
Comparing DP, SMC, and HE in FL Contexts
Privacy Attacks: Inference and Reconstruction
Hands-on Practical: Implementing DP-FedAvg
Chapter 4: Addressing Heterogeneity and Personalization
Sources of Heterogeneity: Statistical and System
Techniques for Handling Non-IID Data Distributions
Clustered Federated Learning Approaches
Meta-Learning for Federated Personalization
Multi-Task Learning in Federated Settings
Model Pruning and Adaptation for Device Constraints
Practice: Simulating Non-IID Data and Mitigation
Chapter 5: Communication Efficiency and System Optimization
Communication Bottlenecks in Federated Learning
Gradient Compression Techniques
Error Accumulation and Compensation Methods
Model Update Compression Strategies
Optimizing Local Computation
Asynchronous Federated Learning Optimizations
Hands-on Practical: Implementing Gradient Quantization
Chapter 6: Federated Learning System Design and Implementation
Architecture of Federated Learning Systems
Overview of FL Frameworks
Simulation vs. Deployment
Monitoring and Debugging Federated Systems
Cross-Silo vs. Cross-Device FL Implementations
Security Considerations in System Deployment
Practice: Setting up a Basic FL Simulation
Techniques for Handling Non-IID Data Distributions
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- Advances and Open Problems in Federated Learning, Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith, 2020 Foundations and Trends® in Machine Learning, Vol. 13 (Now Publishers) DOI: 10.1561/2200000080 - This comprehensive survey provides a broad overview of federated learning, with significant sections dedicated to statistical heterogeneity and Non-IID data as key challenges and corresponding mitigation strategies.
- Clustered Federated Learning: Model-Agnostic Distributed Optimization for Heterogeneous Networks, Soumyabrata Ghosh, Mehrdad Mahdavi, Virginia Smith, 2020 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34 (AAAI) DOI: 10.1609/aaai.v34i07.7077 - This paper introduces Clustered Federated Learning, a framework that addresses severe statistical heterogeneity by dynamically grouping clients with similar data distributions to train separate, specialized models.