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
FedNova: Normalized Averaging for Heterogeneous Systems
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- Communication-Efficient Learning of Deep Networks from Decentralized Data, H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas, 2017 Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 54 (PMLR) DOI: 10.48550/arXiv.1602.05629 - This foundational paper introduced Federated Averaging (FedAvg), a benchmark algorithm for communication-efficient federated learning, which serves as a baseline for methods addressing heterogeneity.
- Federated Learning: Challenges, Methods, and Future Directions, Qiang Yang, Yuanshun Yao, Tian Li, Mao Yang, S. Yu Philip, Jian Li, Cong Shen, Lixin Fan, 2021 IEEE Transactions on Big Data, Vol. 7 (IEEE) DOI: 10.1109/TBDATA.2021.3075727 - A comprehensive survey of federated learning, covering its definition, architecture, various challenges including systems heterogeneity, and discussing current methods and future research directions in the field.
- When Does Federated Averaging Not Benefit From More Local Steps?, Jianyu Wang and Gauri Joshi, 2021 Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), Vol. 130 (PMLR) DOI: 10.1609/aistats.v24i1.18978 - Provides a theoretical analysis of how the number of local steps affects the convergence of Federated Averaging, shedding light on the underlying issues that FedNova aims to address for improved performance in heterogeneous settings.