Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients' private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in dee...

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Veröffentlicht in:	IEEE transactions on information forensics and security Jg. 15; S. 3454 - 3469
Hauptverfasser:	Wei, Kang, Li, Jun, Ding, Ming, Ma, Chuan, Yang, Howard H., Farokhi, Farhad, Jin, Shi, Quek, Tony Q. S., Vincent Poor, H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Agglomeration Algorithms Analytical models Artificial neural networks client selection Clients Computer simulation Convergence convergence performance differential privacy Distributed databases Federated learning information leakage Levels Machine learning Mathematical models Parameters Privacy Scheduling Servers Tradeoffs Training
ISSN:	1556-6013, 1556-6021
Online-Zugang:	Volltext
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