Federated Learning Ebook

弘益人間 · Benefit All Humanity

This ebook teaches federated learning principles that enable collaborative AI development while protecting privacy, embodying the spirit of benefiting all humanity.

CHAPTER 01

Introduction to Federated Learning

Discover the fundamentals of federated learning, its motivation, architecture, and how it differs from traditional centralized machine learning. Learn about the privacy benefits and practical applications.

CHAPTER 02

Privacy-Preserving Techniques

Master differential privacy, secure multi-party computation, homomorphic encryption, and other cryptographic techniques that protect user data in federated learning systems.

CHAPTER 03

Communication Protocols

Explore efficient communication strategies, model compression, gradient quantization, and techniques to minimize bandwidth usage in distributed training environments.

CHAPTER 04

Aggregation Algorithms

Deep dive into federated averaging, weighted aggregation, Byzantine-robust methods, and advanced algorithms for combining model updates from distributed clients.

CHAPTER 05

Client Selection Strategies

Learn optimal strategies for selecting participating clients, handling non-IID data, dealing with system heterogeneity, and ensuring fair participation.

CHAPTER 06

Security and Attack Mitigation

Understand potential attacks on federated learning systems including data poisoning, model inversion, and Byzantine attacks. Learn defense mechanisms and best practices.

CHAPTER 07

Cross-Device vs Cross-Silo Federated Learning

Compare cross-device federated learning (millions of mobile devices) with cross-silo (multiple organizations), their unique challenges, and appropriate solutions.

CHAPTER 08

Production Deployment

Practical guide to deploying federated learning in production: infrastructure setup, monitoring, debugging, scaling, and real-world case studies.