Federated learning systems : towards next-generation AI / Muhammad Habib ur Rehman, Mohamed Medhat Gaber, editors.

Unlimited

Authorized users

Can lend chapters, not whole ebooks

Federated learning systems : towards next-generation AI / Muhammad Habib ur Rehman, Mohamed Medhat Gaber, editors.

9783030706043 electronic book
3030706044 electronic book
9783030706036
3030706036

Cham, Switzerland : Springer, [2021]

English

1 online resource : illustrations (chiefly color).

10.1007/978-3-030-70604-3 doi

Q325.5 .F43 2021

006.3/1

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors' control of their critical data.

Access limited to authorized users.

Description based on online resource; title from digital title page (viewed on October 12, 2022).

Studies in computational intelligence ; v. 965.

Online Resources > Ebooks
All Resources