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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Vydáno v:IEEE transactions on industrial informatics Ročník 16; číslo 3; s. 2134 - 2143
Hlavní autoři: Lu, Yunlong, Huang, Xiaohong, Dai, Yueyue, Maharjan, Sabita, Zhang, Yan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2942179