Description
Book SynopsisChapter 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.
