AuthorsY. Lu, X. Huang, Y. Dai, S. Maharjan and Y. Zhang
TitleDifferentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics
AfilliationCommunication Systems
Project(s)Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications
Publication TypeJournal Article
Year of Publication2020
Journal IEEE Transactions on Industrial Informatics
Pagination2134 - 2143
Date Published03/2020
Publisher IEEE
ISSNPrint ISSN: 1551-3203 Electronic ISSN: 1941-0050

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.

Citation Key27379