Local Differential Privacy based Federated Learning for Internet of Things

Abstract

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, which raises severe location 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 Three-Outputs mechanism is proposed which 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.

arXiv commentThis paper appears in IEEE Internet of Things Journal (IoT-J)

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Cited by in corpus (7)

• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
• Local Differential Privacy and Its Applications: A Comprehensive Survey
• Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs
• Federated Learning -- Methods, Applications and beyond
• SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation
• Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning