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...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 16; H. 3; S. 2134 - 2143
Hauptverfasser:	Lu, Yunlong, Huang, Xiaohong, Dai, Yueyue, Maharjan, Sabita, Zhang, Yan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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