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
Secure Multi-Party Computation (SMC) Protocols for Aggregation
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- How to Share a Secret, Adi Shamir, 1979 Communications of the ACM, Vol. 22 (Association for Computing Machinery) DOI: 10.1145/359168.359176 - The foundational paper introducing Shamir's Secret Sharing, a technique mentioned as an alternative for building robust SMC protocols.
- Introduction to Modern Cryptography, Jonathan Katz, Yehuda Lindell, 2020 (CRC Press) - A standard textbook providing a rigorous treatment of cryptographic primitives, including secret sharing and secure multi-party computation, useful for understanding the underlying theory.
- Privacy-Preserving Federated Learning: A Survey, Xiaokui Wei, Jiashuo Zhang, Huamin Meng, Ruirui Huang, Zhenping Wu, Junping Zhang, 2021 Proceedings of the IEEE, Vol. 109 (IEEE) DOI: 10.1109/JPROC.2021.3117565 - This survey provides an overview of various privacy-preserving techniques in federated learning, including SMC, Differential Privacy, and Homomorphic Encryption, offering a broader context and comparison as discussed in the section.