Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147888
Title: Local differential privacy-based federated learning for Internet of Things
Authors: Zhao, Yang
Zhao, Jun
Yang, Mengmeng
Wang, Teng
Wang, Ning
Lyu, Lingjuan
Niyato, Dusit
Lam, Kwok-Yan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D. & Lam, K. (2021). Local differential privacy-based federated learning for Internet of Things. IEEE Internet of Things Journal, 8(11), 8836-8853. https://dx.doi.org/10.1109/JIOT.2020.3037194
Journal: IEEE Internet of Things Journal 
Abstract: The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users’ location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this paper, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world datasets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.
URI: https://hdl.handle.net/10356/147888
ISSN: 2327-4662
DOI: 10.1109/JIOT.2020.3037194
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2020.3037194
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NTC Journal Articles

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