Federated Learning for Privacy-Preserving AI Systems: Theory, Applications, and Implementation - Samuel Sambasivam - Books - Springer Nature Switzerland AG - 9783032293770 - February 24, 2027

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Samuel Sambasivam
Federated Learning for Privacy-Preserving AI Systems: Theory, Applications, and Implementation Samuel Sambasivam

Name: Federated Learning for Privacy-Preserving AI Systems: Theory, Applications, and Implementation
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Federated Learning for Privacy-Preserving AI Systems: Theory, Applications, and Implementation

Samuel Sambasivam

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.

Media	Books Hardcover Book (Book with hard spine and cover)
To be released	February 24, 2027
ISBN13	9783032293770
Publishers	Springer Nature Switzerland AG
Pages	373
Dimensions	150 × 220 × 20 mm · 592 g (Weight (estimated))

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