Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promis...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on industrial informatics Vol. 16; no. 3; pp. 2134 - 2143
Main Authors:	Lu, Yunlong, Huang, Xiaohong, Dai, Yueyue, Maharjan, Sabita, Zhang, Yan
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Ad hoc networks Algorithms Artificial intelligence Data models Data sharing Edge computing Federated learning Informatics local differential privacy (LDP) Machine learning Mobile computing Privacy Resource management Robustness (mathematics) Security Servers Task analysis Training Urban development urban informatics Urbanization Vehicles
ISSN:	1551-3203, 1941-0050
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!