£88.65

About as cheap as it gets — within 5% of the all-time low it hit 1 month ago.

£89 today · all-time low £88 (Aug 2026) · usually £89

Springer Federated Edge Learning: Algorithms, Architectures and Trustworthiness

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£89.03 £88.16 £88.35 £88.54 £88.73 £88.92 £89.11 27 July 2026 13 August 2026 31 August 2026 18 September 2026 06 October 2026
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Price distribution over 72 days • 2 price levels

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47 days · current 25 days · current 0 12 24 35 47 £88 £89 Days at Price

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Most common price: £88 (47 days, 65.3%)

Price range: £88 - £89

Price levels: 2 different prices over 72 days

Description

Federated Edge Learning: Algorithms, Architectures and Trustworthiness is a comprehensive technical resource from Springer that explores the intersection of distributed machine learning and edge computing infrastructure. This book examines the fundamental algorithms and architectural patterns that enable federated learning systems to operate across decentralized networks without centralizing sensitive data. Readers gain in-depth knowledge of trustworthiness mechanisms, privacy-preserving techniques, and security considerations essential for deploying federated edge learning in real-world wireless network environments. The publication addresses key challenges in maintaining model accuracy, reducing communication overhead, and ensuring data protection across heterogeneous edge devices. Ideal for researchers, practitioners, and engineers working with distributed systems, wireless networks, and machine learning applications, this Springer resource provides both theoretical foundations and practical implementations for building secure, efficient federated learning architectures.

Key Features

Federated Edge Learning covers advanced algorithms designed for distributed machine learning across wireless network infrastructures.

Explores architectural frameworks for implementing federated learning systems that preserve data privacy on edge devices.

Addresses trustworthiness and security mechanisms critical for protecting sensitive information in decentralized learning environments.

Springer publication provides comprehensive technical depth for researchers and practitioners in networking and distributed computing.

Examines communication efficiency, model convergence, and heterogeneous device management in federated edge learning deployments.

Combines theoretical foundations with practical guidance for designing secure wireless network-based machine learning systems.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 August 2025
Listed Since
14 May 2025

Barcode

No barcode data available

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