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Federated learning systems = towards...

Rehman, Muhammad Habib ur.

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Federated learning systems = towards privacy-preserving distributed AI /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Federated learning systems/ edited by Muhammad Habib ur Rehman, Mohamed Medhat Gaber.
其他題名:	towards privacy-preserving distributed AI /
其他作者:	Rehman, Muhammad Habib ur.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xviii, 165 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1.Empowering Federated Learning for Massive Models with NVIDIA FLARE -- Chapter 2.Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications -- Chapter 3.Client Selection in Federated Learning: Challenges, Strategies, and Contextual Considerations -- Chapter 4.A Review of Secure Gradient Compression Techniques for Federated Learning in the Internet of Medical Things -- Chapter 5.Federated Learning for Recommender Systems: Advances and perspectives -- Chapter 6.The Missing Subject in Health Federated Learning: Preventive and Personalized Care -- Chapter 7.Privacy-Enhancing Technologies for Federated Learning -- Chapter 8.Collaborative Defense: Federated Learning for Intrusion Detection Systems.
Contained By:	Springer Nature eBook
標題:	Federated learning (Machine learning) -
電子資源:	https://doi.org/10.1007/978-3-031-78841-3
ISBN:	9783031788413

Federated learning systems = towards privacy-preserving distributed AI /
Federated learning systemstowards privacy-preserving distributed AI /[electronic resource] :edited by Muhammad Habib ur Rehman, Mohamed Medhat Gaber. - Cham :Springer Nature Switzerland :2025. - xviii, 165 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 8321860-9503 ;. - Studies in computational intelligence ;v. 832..

Chapter 1.Empowering Federated Learning for Massive Models with NVIDIA FLARE -- Chapter 2.Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications -- Chapter 3.Client Selection in Federated Learning: Challenges, Strategies, and Contextual Considerations -- Chapter 4.A Review of Secure Gradient Compression Techniques for Federated Learning in the Internet of Medical Things -- Chapter 5.Federated Learning for Recommender Systems: Advances and perspectives -- Chapter 6.The Missing Subject in Health Federated Learning: Preventive and Personalized Care -- Chapter 7.Privacy-Enhancing Technologies for Federated Learning -- Chapter 8.Collaborative Defense: Federated Learning for Intrusion Detection Systems.

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.

ISBN: 9783031788413

Standard No.: 10.1007/978-3-031-78841-3doiSubjects--Topical Terms:

3781177
Federated learning (Machine learning)

LC Class. No.: Q325.65

Dewey Class. No.: 006.31

Federated learning systems = towards privacy-preserving distributed AI /
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520 $a 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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