{"product_id":"9783032293770","title":"Federated Learning for Privacy-Preserving AI Systems: Theory, Applications, and Implementation","description":"\u003ch1\u003eFederated Learning for Privacy-Preserving AI Systems: Theory, Applications, and Implementation\u003c\/h1\u003e \u003ch2\u003eSambasivam, Samuel\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis book presents a rigorous and comprehensive treatment of federated learning as a foundational paradigm for privacy-preserving artificial intelligence. Integrating theoretical principles with implementation strategies and domain-specific applications, it offers a unified framework for understanding the design, optimization, and deployment of distributed AI systems in privacy-sensitive environments.\u003c\/p\u003e\n\u003cp\u003eThe volume systematically examines the architectures and operational models of horizontal, vertical, cross-device, and cross-silo federated learning. Core optimization algorithms—including FedAvg, FedProx, personalized federated learning methods, and asynchronous federated approaches—are analyzed in detail, with particular attention to convergence behavior under non-IID and heterogeneous data distributions. The text further explores the mathematical and systems foundations that enable secure and trustworthy collaboration across decentralized environments.\u003c\/p\u003e\n\u003cp\u003eA substantial portion of the book is devoted to privacy-preserving and security-enhancing mechanisms that underpin modern federated systems. Topics include differential privacy, secure aggregation, homomorphic encryption, secure multi-party computation, Byzantine-resilient aggregation, and adversarial robustness. These techniques are evaluated not only from a theoretical perspective but also in terms of their practical implications for scalability, communication efficiency, model utility, and deployment.\u003c\/p\u003e\n\u003cp\u003eTo bridge theory and practice, the book presents detailed application studies in financial systems, cybersecurity for zero-day attack detection, and healthcare diagnostics. Each case study includes experimental design, dataset considerations, baseline comparisons, implementation workflows, performance evaluation, and critical discussion of practical challenges and research opportunities. A dedicated design science chapter further guides readers through requirements analysis, system architecture, deployment strategies, and operational best practices for enterprise-scale federated AI systems.\u003c\/p\u003e\n\u003cp\u003eDesigned for graduate students, researchers, and industry practitioners, this text provides a pedagogically integrated resource that combines analytical rigor with practical relevance. Readers will benefit from worked examples, implementation guidance, comparative analyses, and end-of-chapter exercises that support both academic study and real-world application. By unifying theoretical foundations, privacy-preserving methodologies, and production-oriented considerations within a single volume, this book serves as an authoritative reference for the next generation of secure and decentralized AI systems.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-08-18\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783032293770\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 362\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":50185179594892,"sku":"9783032293770","price":58.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032293770.jpg?v=1779974489","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783032293770","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}