This book offers a systematic, self-contained treatment of privacy-preserving federated learning (PPFL). It covers the complete arc from foundations to deployment. This book also illustrates how federated learning works and where it leaks; the privacy threats that matter in practice, from gradient inversion to membership inference; the protection techniques—differential privacy, secure multi-party computation, homomorphic encryption, and trusted execution environment.  The authors provide the metrics and benchmarks needed to evaluate them and advanced topics including federated unlearning, vertical federated learning, and privacy-preserving fine-tuning of large language models.

Beyond algorithms, this book examines real-world systems and governance: open-source frameworks such as Flower, FATE, and NVIDIA FLARE; deployments in healthcare, finance, and smart cities. The regulatory landscape shaped by GDPR, HIPAA, CCPA, and PIPL. Framework comparisons, threat models, and case studies connect theory to engineering and policy throughout are included in this book as well. 

Graduate students and researchers will find a rigorous entry point into the field when reading this book as well as engineers and system architects, concrete deployment guidance and policy professionals.  Chapters are largely self-contained, supporting role-based reading paths and use as both course text and desk reference.

Runhua Xu is a Professor in the School of Computer Science and Engineering at Beihang University, Beijing, China. His research centers on privacy-enhancing technologies and federated learning, spanning privacy-preserving collaborative learning, applied cryptography, and security and privacy in AI systems. Before joining Beihang, he was a Research Staff Member at IBM Research, where he worked on AI security and privacy solutions, and he received his Ph.D. from the University of Pittsburgh. His work appears at venues including ACM CCS, USENIX Security, NeurIPS, AAAI, IEEE TDSC, and IEEE TIFS, and has been recognized with an ACM CCS 2023 Distinguished Paper Award and a Best Paper Award at IEEE CLOUD 2022.

James B.D. Joshi is a Professor in the School of Computing and Information at the University of Pittsburgh and the founding director of the Laboratory of Education and Research on Security Assured Information Systems (LERSAIS), Pitt's National Center of Academic Excellence in Cybersecurity. His research spans cybersecurity and privacy, with a focus on advanced access control, distributed systems security and privacy, trust, and security and privacy in AI/ML. He is a Fellow of AAAS and the IEEE and an ACM Distinguished Member, and he received his Ph.D. in Computer Engineering from Purdue University.