Explains federated learning and how it integrates AI technologies allowing multiple collaborators to build a robust machine-learning model using a large dataset. Describes benefits of federated learning, covering data privacy, data security, data access rights etc. Analyses common challenges, and attack strategies affecting FL systems.
Inhaltsverzeichnis
1. The Evolution of Machine Learning: From Centralized to Distributed 2. Types of Federated Learning and Aggregation Techniques 3. Federated Learning for IoT/Edge/Fog Computing Systems 4. Adopting Federated Learning for Software-Defined Networks 5. Federated Learning in the Internet of Medical Things 6. Federated Learning Approaches for Intrusion Detection Systems: An Overview 7. Exploring Communication Efficient Strategies in Federated Learning Systems 8. Federated Learning and Privacy, Challenges, Threat and Attack Models, and Analysis 9. Analyzing Federated Learning from a Security Perspective 10. Blockchain Integrated Federated Learning in Edge/Fog/Cloud Systems for IoT-Based Healthcare Applications: A Survey 11. Incentive Mechanism for Federated Learning 12. Protected Shot-Based Federated Learning for Facial Expression Recognition
