Federated Learning Systems : Towards Privacy-Preserving Distributed AI / edited by Muhammad Habib ur Rehman, Mohamed Medhat Gaber.

Format:

Book

Contributor:

Rehman, Muhammad Habib ur., Editor.

Gaber, Mohamed Medhat, Editor.

Series:

Studies in Computational Intelligence, 1860-9503 ; 832

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Computational Intelligence.

Artificial Intelligence.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Physical Description:

1 online resource (XVIII, 165 p. 30 illus., 25 illus. in color.)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-78841-9

OCLC:

1524422040