Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

By Yi Liu, James J. Q. Yu, Jiawen Kang, Dusist Niyato, Shuyu Zhang. 2020

549 citations recorded by the source corpus.

Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets may contain lots of user’s private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning (FL) and propose an FL-based gated recurrent unit neural network algorithm (FedGRU) for traffic flow prediction (TFP). FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a federated averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a joint announcement protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for TFP by grouping the organizations into clusters before applying the FedGRU algorithm. Extensive case studies on a real-world data set demonstrate that FedGRU can produce predictions that are merely 0.76 km/h worse than the state of the art in terms of mean average error under the privacy preservation constraint, confirming that the proposed model develops accurate traffic predictions without compromising the data privacy.

Subfields: Artificial Intelligence, Building and Construction, Electrical and Electronic Engineering

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Citations in the corpus

FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “…but are not limited to, computer vision [77, 78], natural language processing [79, 80, 81, 82, 83], ﬁnance [74, 35, 84], transportation [85, 86, 87, 88, 89, 90, 91, 92, 93, 94], digital health [95, 96, 97, 98, 99, 100, 101], recommendation [102, 103, 104, 105, 106, 107], robotics [108, 109], and…”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “An ingenious way to add privacy protections in FL was proposed in [130].”
Deep Anomaly Detection for Time-Series Data in Industrial IoT: A Communication-Efficient On-Device Federated Learning Approach (2020, background): “lenges, a collaboratively distributed deep learning paradigm, called federated deep learning, was proposed for edge devices to train a global model while keeping the training data sets locally without sharing raw training data [18].”
Federated learning for 6G communications: Challenges, methods, and future directions (2020, background): “For example, industries such as remote surgery [14], cloud PLC [15], and intelligent transportation systems [16] will be empowered by the new service model to provide users with better services.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “FL is also a very useful solution to replace traditional centralized ML approaches in traffic prediction tasks [10], [26] by running ML IoT Devices”
Federated Learning: Opportunities and Challenges (2021, methodology): “Works such as [5, 21] try to address this problem by modifying the model aggregation methods.”
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, background): “To overcome the trust problem over decentralized network, blockchain technology provides the trust mechanism in various ways [13], [25]–[28].”