Local Differential Privacy based Federated Learning for Internet of Things

TL;DR

This paper combines federated learning with local differential privacy mechanisms to enhance privacy and reduce communication costs in Internet of Vehicles applications, proposing novel gradient perturbation methods.

Contribution

It introduces four new LDP mechanisms, including the Three-Outputs and PM-OPT, and a hybrid approach to improve privacy-utility trade-offs in IoV federated learning.

Findings

Three-Outputs mechanism achieves high accuracy with small privacy budgets.

Proposed mechanisms reduce communication costs via encoding outputs with two bits.

Hybrid mechanism outperforms individual methods in utility.

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.