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 Pruning and Adaptation for Device Constraints
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- Once-for-All: Train One Network and Specialize It for Efficient Deployment, Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, Song Han, 2020 International Conference on Learning Representations DOI: 10.48550/arXiv.1908.09791 - This paper introduces the Once-for-All (OFA) network architecture, a flexible framework that allows training a single large network from which numerous specialized sub-networks can be extracted and deployed efficiently without further retraining, directly relevant to model adaptation.
- Distilling the knowledge in a neural network, Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015 arXiv preprint arXiv:1503.02531 DOI: 10.48550/arXiv.1503.02531 - This foundational paper introduces the concept of knowledge distillation, a method for transferring knowledge from a large, complex 'teacher' model to a smaller, more efficient 'student' model, which is a core technique for model adaptation.
- Pruning Filters for Efficient ConvNets, Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf, 2017 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1608.08710 - This paper proposes a structured pruning method focusing on removing entire filters from convolutional neural networks, demonstrating how to achieve significant model compression while maintaining accuracy, directly related to the provided example of filter pruning.