Federated Learning

NEP 2020 Compliant

Federated Learning (FL) emerges as a powerful and elegant solution to this modern predicament. It is a distributed machine learning approach that allows models to be trained on decentralized data residing on local devices or servers, without requiring the raw data to be shared or moved. Instead, only the model updates (or gradients) are exchanged and aggregated on a central server. This preserves data privacy by design, reduces communication costs compared to transferring raw data, and enables AI to be deployed closer to the source of data – the edge. Federated Learning is not merely an optimization; it is a paradigm shift towards privacy-preserving, secure, and efficient collaborative AI.

This book, "Federated Learning," is crafted with the aim of providing a comprehensive, up-to-date, and practical guide to this rapidly evolving field for undergraduate (B.Tech) and postgraduate (M.Tech) students of Computer Science, Data Science, Artificial Intelligence, and related disciplines across India and globally. The field of Federated Learning is dynamic, sitting at the intersection of Machine Learning, Distributed Systems, Cryptography, Privacy, and Security. As such, a thorough understanding requires delving into multiple interconnected domains. We have strived to cover the foundational principles, state-of-the-art algorithms, practical challenges, real-world applications, and future research directions within a structured framework suitable for academic study and future innovation.

In alignment with the progressive vision of India's National Education Policy (NEP) 2020, this book adopts several pedagogical and thematic approaches. The NEP 2020 emphasizes holistic and multidisciplinary education, fostering critical thinking, creativity, and problem-solving skills, while also stressing the importance of ethical considerations and industry relevance. Federated Learning, by its very nature, embodies these principles.

I believe this book will serve as an invaluable resource for students and instructors alike. For students, it is intended to be a primary textbook, providing a solid theoretical foundation and practical insights into Federated Learning. For instructors, it offers a structured curriculum with flexibility to emphasize specific areas like privacy, systems, or applications based on course objectives. Researchers and industry professionals seeking to understand or implement FL systems will also find it a useful reference.