| Description |
1 online resource (353 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- About the Editors -- Table of Contents -- Contributors -- Abbreviations -- Preface -- 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 -- Index |
| Summary |
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 |
| Notes |
Description based upon print version of record |
| Form |
Electronic book
|
| Author |
Ouaissa, Mariya
|
|
Nair, Akarsh K
|
| ISBN |
9781040088593 |
|
1040088597 |
|
