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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. Real-World Deployment Considerations

Monitoring and Debugging Federated Systems

Cross-Silo vs. Cross-Device FL Implementations

Security Considerations in System Deployment

Practice: Setting up a Basic FL Simulation

Federated Learning Foundations Revisited

To effectively build upon federated learning (FL), this chapter revisits its core concepts. We start by recapping the standard FL workflow and its basic principles.

You will examine the inherent difficulties faced in federated settings, specifically statistical heterogeneity (non-IID data) and systems heterogeneity (variations in client hardware, network, and availability). We will define the mathematical objective function typically used in federated optimization, often expressed as minimizing a global loss aggregated across clients:

F(w) = \sum_{k=1}^N p_k F_k(w)

where $F_k(w)$ is the local objective for client $k$ and $p_k$ represents its contribution weight.

Further sections compare synchronous and asynchronous training paradigms, analyze potential security and privacy threats within the FL threat model, and discuss essential metrics and methodologies for evaluating the performance, efficiency, and fairness of these distributed systems. This review establishes the necessary groundwork for the advanced techniques covered in subsequent chapters.

Sections

1.1 Federated Learning Principles: A Recap
1.2 Challenges in Federated Environments
1.3 Mathematical Formulation of Federated Optimization
1.4 Synchronous vs. Asynchronous Federated Learning Models
1.5 Threat Models in Federated Learning
1.6 Evaluating Federated Learning Systems

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