Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

By Yunlong Lu, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, Yan Zhang. IEEE Transactions on Industrial Informatics, 2020

315 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/38fbc724bafeb1b4908cd9608f866e2bd76871ca

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Citations in the corpus

A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, unknown): “Xie et al. [96] proposed asynchronous federated optimization, [159] discussed on the asynchronous FL for geospatial applications, [160]–[162] proposed a DP-based asynchronous FL strategy for MEC, and [163] presented a blockchain-based secure data sharing strategy for asynchronous FL.”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, background): “[110] propose an asynchronous federated learning scheme for resource sharing in vehicular IoT.”
A Survey on federated learning* (2020, background): “These includes cases in industry applications [2][3] or mobile devices [4] that data are not available to be aggregated with legal concern.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “WHAT RB1 [75, 160, 192] [215, 218, 221] [29, 113, 157] [144, 199, 227] [63, 70, 193] [16, 109, 225] [69, 78, 110] [20, 52, 57] [32, 165, 197] [198, 199, 224] RB2 [129, 165, 196] [26, 111, 221] [157, 169, 224] [164, 227] [40, 78, 193] [116, 168, 174] [69, 172, 193] [20, 81, 216] [56, 165, 197] [69, 1”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “[53] Vehicular data sharing DFL Decision tree Vehicles MBSs A differential privacy-based FL model for secure vehicular data sharing.”