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Springer Federated and Transfer Learning: Recent Research & Applications

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£111.78 £98.82 £101.65 £104.48 £107.30 £110.13 £112.96 28 July 2026 10 August 2026 23 August 2026 05 September 2026 19 September 2026
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Description

This comprehensive book examines cutting-edge research in federated and transfer learning, two rapidly advancing areas within machine learning. The collection addresses learning from decentralised data sources, transferring knowledge across different domains, and key theoretical challenges including privacy protection and incentive mechanisms in federated systems. The work explores critical connections between federated learning, transfer learning, and reinforcement learning, reflecting the machine learning community's growing focus on these methods. Designed for students and researchers, this volume in the Adaptation, Learning, and Optimization series (volume 27) presents recent breakthroughs and practical applications across multiple fields where federated and transfer learning have demonstrated significant success and real-world impact.

Key Features

Covers recent research works on learning from decentralised data sources and federated systems.

Addresses privacy and incentive factors in federated learning with theoretical foundations.

Explores the connection between federated learning, transfer learning, and reinforcement learning.

Presents applications across multiple fields demonstrating federated and transfer learning success.

Part of the Springer Adaptation, Learning, and Optimization series (volume 27) for academic credibility.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 October 2022
Listed Since
16 June 2022

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No barcode data available

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