Atnaujinkite slapukų nuostatas

Federated Learning Systems: Towards Privacy-Preserving Distributed AI [Kietas viršelis]

Edited by , Edited by
  • Formatas: Hardback, 165 pages, aukštis x plotis: 235x155 mm, 25 Illustrations, color; 5 Illustrations, black and white; XVIII, 165 p. 30 illus., 25 illus. in color., 1 Hardback
  • Serija: Studies in Computational Intelligence 832
  • Išleidimo metai: 27-Apr-2025
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031788400
  • ISBN-13: 9783031788406
Kitos knygos pagal šią temą:
Federated Learning Systems: Towards Privacy-Preserving Distributed AIDidesnis paveikslėlis
  • Formatas: Hardback, 165 pages, aukštis x plotis: 235x155 mm, 25 Illustrations, color; 5 Illustrations, black and white; XVIII, 165 p. 30 illus., 25 illus. in color., 1 Hardback
  • Serija: Studies in Computational Intelligence 832
  • Išleidimo metai: 27-Apr-2025
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031788400
  • ISBN-13: 9783031788406
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783031788406.html
Raktažodžiai:
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.

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.