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Federated Learning Systems: Towards Privacy-Preserving Distributed AI

2026-04-20 分类：计算机与互联网 评论(0)

Federated Learning Systems: Towards Privacy-Preserving Distributed AI (Studies in Computational Intelligence, 832)

Federated Learning Systems: Towards Privacy-Preserving Distributed AI (Studies in Computational Intelligence, 832)

Author: Muhammad Habib ur Rehman (Editor), Mohamed Medhat Gaber (Editor)

Publisher: ‎ Springer

Edition: N/A

Publication Date: 2025-04-27

Language: English

Paperback: 183 pages

ISBN-10: 3031788400

ISBN-13: 9783031788406

Book Description

This book dives deep into both industry implementations and cutting-edge research driving the Federated Learning (FL) landscape forward. FL enables decentralized model training, preserves data privacy, and enhances security without relying on centralized datasets. Industry pioneers like NVIDIA have spearheaded the development of general-purpose FL platforms, revolutionizing how companies harness distributed data. Alternately, for medical AI, FL platforms, such as FedBioMed, enable collaborative model development across healthcare institutions to unlock massive value.

Research advances in PETs highlight ongoing efforts to ensure that FL is robust, secure, and scalable. Looking ahead, federated learning could transform public health by enabling global collaboration on disease prevention while safeguarding individual privacy. From recommendation systems to cybersecurity applications, FL is poised to reshape multiple domains, driving a future where collaboration and privacy coexist seamlessly.

From the Back Cover

This book dives deep into both industry implementations and cutting-edge research driving the Federated Learning (FL) landscape forward. FL enables decentralized model training, preserves data privacy, and enhances security without relying on centralized datasets. Industry pioneers like NVIDIA have spearheaded the development of general-purpose FL platforms, revolutionizing how companies harness distributed data. Alternately, for medical AI, FL platforms, such as FedBioMed, enable collaborative model development across healthcare institutions to unlock massive value.

Research advances in PETs highlight ongoing efforts to ensure that FL is robust, secure, and scalable. Looking ahead, federated learning could transform public health by enabling global collaboration on disease prevention while safeguarding individual privacy. From recommendation systems to cybersecurity applications, FL is poised to reshape multiple domains, driving a future where collaboration and privacy coexist seamlessly.

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