Reliable Federated Learning for Mobile Networks

Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training d...

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Vydané v:IEEE wireless communications Ročník 27; číslo 2; s. 72 - 80
Hlavní autori: Kang, Jiawen, Xiong, Zehui, Niyato, Dusit, Zou, Yuze, Zhang, Yang, Guizani, Mohsen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Cognitive tasks
Consortia
Cryptography
Data models
Data privacy
Electronic devices
Federated learning
Iterative methods
Machine learning
Metasearch
Mobile handsets
Network reliability
Numerical analysis
Performance enhancement
Public relations
Reliability analysis
Reputation management
Task analysis
Training data
Wireless networks
Workers
ISSN:1536-1284, 1558-0687
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Popis
Shrnutí:Federated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, for example, the data poisoning attack, or unintentionally, for example, low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1536-1284
1558-0687
DOI:10.1109/MWC.001.1900119