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
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Abstract	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.
AbstractList	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.
Author	Lu, Yunlong Zhang, Yan Maharjan, Sabita Huang, Xiaohong Dai, Yueyue
Author_xml	– sequence: 1 givenname: Yunlong orcidid: 0000-0001-9552-5130 surname: Lu fullname: Lu, Yunlong email: yunlong.lu@ieee.org organization: Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 2 givenname: Xiaohong orcidid: 0000-0002-7275-2274 surname: Huang fullname: Huang, Xiaohong email: huangxh@bupt.edu.cn organization: Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China – sequence: 3 givenname: Yueyue orcidid: 0000-0002-2163-987X surname: Dai fullname: Dai, Yueyue email: yueyuedai@ieee.org organization: University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Sabita orcidid: 0000-0002-4616-8488 surname: Maharjan fullname: Maharjan, Sabita email: sabita@simula.no organization: Simula Metropolitan Center for Digital Engineering and the University of Oslo, Oslo, Norway – sequence: 5 givenname: Yan orcidid: 0000-0002-8561-5092 surname: Zhang fullname: Zhang, Yan email: yanzhang@ieee.org organization: Department of Informatics, University of Oslo, Oslo, Norway
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SubjectTerms	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
Title	Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics
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