Local Differential Privacy-Based Federated Learning for Internet of Things

By Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusist Niyato, Kwok-Yan Lam. 2020

414 citations recorded by the source corpus.

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 article, 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 data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.

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

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Citations in the corpus

Federated Model Distillation with Noise-Free Differential Privacy (2020, methodology): “In order to protect training data privacy in FL, various privacy protection techniques have been proposed in the literature [Geyer et al., 2017; McMahan et al., 2018; Bonawitz et al., 2017; Wang et al., 2019b; Zhao et al., 2020; Sun et al., 2021; Lyu et al., 2020].”
A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, positive): “Geyer et al. [40], McMahan et al. [56], and Zhao et al. [200] applied DP to FL to achieve a global DP, but the hyperparameters of these approaches affect the communication and model accuracy.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “[102] IoT privacy preservation HFL DNN Vehicles Cloud server An FL-differential privacy-based scheme for privacy enhancement in vehicular IoT.”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, background): “[167], [203] for data privacy protection to ensure the parties cannot know whether an individual record participates in the learning or not.”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, methodology): “• In FLchain, attacks can make attempt to train a lo-cal model using designed falsiﬁed data to replace the global model and modify parameter values during model transmission, aiming to manipulate the training output.”
Privacy and Robustness in Federated Learning: Attacks and Defenses (2020, background): “Other approaches that also considered to apply LDP to FL can only support shallow models such as logistic regression and only focus on simple tasks and datasets [83], [84], [85].”
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges (2020, background): “sending it to the global aggregation server [26], [104]–[108].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, positive): “Also, in [68], [69], the addition of noise to raw data and the use of differential privacy (DP) methods were shown effective in privacy protection.”