Description
| Product ID: | 9780443190377 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | GB |
| Title: | Federated Learning |
| Subtitle: | Theory and Practice |
| Authors: | Author: Lam M. Nguyen, Trong Nghia Hoang, Pin-Yu Chen |
| Page Count: | 434 |
| Subjects: | Artificial intelligence, Artificial intelligence, Expert systems / knowledge-based systems, Machine learning, Expert systems / knowledge-based systems, Machine learning |
| Description: | Select Guide Rating Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data. Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors. |
| Imprint Name: | Academic Press Inc |
| Publisher Name: | Elsevier Science Publishing Co Inc |
| Country of Publication: | GB |
| Publishing Date: | 2024-02-15 |
