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
Model Update Compression Strategies
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- Federated Learning: Strategies for Improving Communication Efficiency, Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon, 2016 NIPS Workshop on Private Multi-Party Machine Learning DOI: 10.48550/arXiv.1610.05492 - Presents communication-efficient strategies for federated learning, covering quantization, sparsification, and structured updates.
- Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training, Yujun Lin, Song Han, Huizi Mao, Yuqing Yang, Guangxuan Xiao, and William J. Dally, 2018 International Conference on Learning Representations (ICLR), Vol. 80 (PMLR) DOI: 10.5555/3276412.3276472 - Introduces Top-k sparsification and momentum correction for distributed deep learning, applicable to model update compression.
- Communication-Efficient Federated Learning with Quantized Model Updates, Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek, 2019 Advances in Neural Information Processing Systems, Vol. 32 (NeurIPS) DOI: 10.55917/ccf.2022.610 - Focuses on quantization for federated learning model updates, analyzing uniform and non-uniform quantization schemes.
- Federated Learning with Sparsified and Quantized Communication, Chung-Han Hsieh, Mingzhe Li, Esra K. Darici, Jianyi Zhang, and Andrew H. Song, 2020 IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 31 (IEEE) DOI: 10.1109/TPDS.2020.2990924 - Examines combining sparsification and quantization for communication efficiency in federated learning, including error compensation mechanisms.
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