Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Privacy-Preserving Federated Learning: Foundations, Techniques, and Practice

Privacy-Preserving Federated Learning: Foundations, Techniques, and Practice

Xu, Runhua
Joshi, James B. D.

Hardcover

Series: Advances in Information Security, Book 96

General ComputersProbability & StatisticsComputer Security

PREORDER - Expected ship date February 24, 2027

ISBN10: 3032426782
ISBN13: 9783032426789
Publisher: Springer
Published: Feb 24 2027
Pages: 250
Language: English

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.

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