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
Privacy Attacks: Inference and Reconstruction
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- Inverting Gradients-How much information do gradients reveal?, Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, and Michael Rath, 2020 Advances in Neural Information Processing Systems (NeurIPS) 33 (NeurIPS) DOI: 10.5591/00021 - This research presents more advanced and robust methods for reconstructing training data from gradients, further emphasizing the privacy risks in gradient sharing.
- Advances and Open Problems in Federated Learning, Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao, 2021 Foundations and Trends® in Machine Learning, Vol. 14 (Now Publishers) DOI: 10.1561/2200000083 - A comprehensive review of federated learning, dedicating significant sections to privacy attacks, defenses (Differential Privacy, Secure Multi-Party Computation, Homomorphic Encryption), and open challenges in the field.