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
Adaptive Federated Optimization Techniques
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- Adaptive Federated Optimization, Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, H. Brendan McMahan, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2003.00295 - Introduces FedAdam and FedYogi, extending adaptive optimization to federated learning for improved convergence.
- Oort: Efficient Federated Learning via Guided Participant Selection, Fan Lai, Xiang Li, Xiang Liu, Chenghong Li, Maomao Zhang, Minghao Li, Jian Li and Mosharaf Chowdhury, 2021 Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI '21) (USENIX) DOI: 10.48550/arXiv.2104.09033 - Presents Oort, a client selection method that prioritizes clients based on data utility and resource availability to optimize FL performance.
- Adaptive Weighting for Federated Learning with Heterogeneous Data, Shilei Duan, Hong Zeng, Jingsong Ren, Bo Liu and Jinkui Chen, 2020 International Conference on Learning Representations - Proposes a method for dynamically adjusting client aggregation weights in federated learning to handle data heterogeneity.