Description

Book Synopsis

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.

Federated Learning Systems

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    A Hardback by Muhammad Habib ur Rehman

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      Publisher: Springer
      Publication Date: Publication Date: 05/04/2025
      ISBN13: 9783031788406, 978-3031788406
      ISBN10:
      Also in:
      Computing Artificial intelligence (AI)

      Description

      Book Synopsis

      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.

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