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
Optimizing Local Computation
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- Communication-Efficient Learning of Deep Networks from Decentralized Data, H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas, 2017 Proceedings of the 20 th International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 54 (JMLR: W&CP) DOI: 10.48550/arXiv.1602.05629 - Introduces the Federated Averaging (FedAvg) algorithm and discusses its communication efficiency, including the role of local epochs (E) and client sampling (K).
- Federated Optimization in Heterogeneous Networks, Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith, 2020 Proceedings of Machine Learning and Systems (MLSys) DOI: 10.48550/arXiv.1812.06127 - Proposes FedProx, an algorithm that addresses client drift in federated learning by introducing a proximal term to local objective functions, allowing for more local computation on Non-IID data.
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning, Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh, 2020 Proceedings of the International Conference on Machine Learning (ICML) DOI: 10.48550/arXiv.1910.06378 - Presents SCAFFOLD, an algorithm that mitigates client drift using control variates, enabling more efficient local computation in federated learning with heterogeneous data.