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

Format:

Book

Contributor:

Rehman, Muhammad Habib ur, editor.

Gaber, Mohamed Medhat, editor.

Series:

Studies in computational intelligence ; Volume 965.

Studies in Computational Intelligence ; Volume 965

Language:

English

Subjects (All):

Machine learning.

Computational intelligence.

Physical Description:

1 online resource (207 pages)

Place of Publication:

Cham, Switzerland : Springer, [2021]

Summary:

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.

Notes:

Description based on print version record.

ISBN:

3-030-70604-4

OCLC:

1256542411