Price: $84.99
(as of Dec 24,2024 04:27:17 UTC – Details)

Publisher ‏ : ‎ Springer; 1st ed. 2020 edition (November 26, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 296 pages
ISBN-10 ‏ : ‎ 3030630757
ISBN-13 ‏ : ‎ 978-3030630751
Item Weight ‏ : ‎ 1.01 pounds
Dimensions ‏ : ‎ 6.1 x 0.67 x 9.25 inches

Federated Learning: Privacy and Incentive (Lecture Notes in Computer Science)

Federated Learning is an emerging field in machine learning that aims to train models across multiple decentralized devices which keep data private on the device itself, rather than sending it to a central server. This approach has the potential to address privacy concerns associated with traditional centralized machine learning models.

In this lecture notes series in Computer Science, we delve into the key concepts of Federated Learning, focusing on the crucial aspects of privacy and incentive. We explore how Federated Learning enables data owners to collaborate on model training without sharing their raw data, thus preserving privacy.

Furthermore, we discuss the importance of providing incentives for participants to actively participate in Federated Learning. Incentive mechanisms play a vital role in motivating users to contribute their data and computational resources to the model training process. We examine various incentive schemes and their implications for the success of Federated Learning systems.

Join us as we explore the intersection of privacy and incentive in Federated Learning, and learn how this innovative approach is shaping the future of machine learning.
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