Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach

Abstract

Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of datasets gathered by governments and organizations. However, these datasets 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 trade-off between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning and propose a Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. 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 traffic flow prediction by grouping the organizations into clusters before applying FedGRU algorithm. Through extensive case studies on a real-world dataset, it is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models, which confirm that FedGRU can achieve accurate and timely traffic prediction without compromising the privacy and security of raw data.

This paper is in the second round of under review of the IEEE Internet of Things Journal

References in corpus (13)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Semi-Supervised Classification with Graph Convolutional Networks
• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Learning with Non-IID Data
• Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
• Towards Federated Learning at Scale: System Design
• Federated Optimization in Heterogeneous Networks
• Fair Resource Allocation in Federated Learning
• A generic framework for privacy preserving deep learning
• Practical Secure Aggregation for Federated Learning on User-Held Data
• Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices
• FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
• Reliable Federated Learning for Mobile Networks

Cited by in corpus (37)

• Federated Learning for Internet of Things: A Comprehensive Survey
• Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing
• Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach
• FedML: A Research Library and Benchmark for Federated Machine Learning
• Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
• Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms
• Deep Learning for Time Series Forecasting: A Survey
• FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
• FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
• Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
• Federated Learning for 5G Base Station Traffic Forecasting
• Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
• Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations
• Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
• Communication Efficiency in Federated Learning: Achievements and Challenges
• Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things
• Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions
• BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction
• Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks
• Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction
• CADeSH: Collaborative Anomaly Detection for Smart Homes
• A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
• B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction
• A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
• Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning
• Mobility-Aware Decentralized Federated Learning with Joint Optimization of Local Iteration and Leader Selection for Vehicular Networks
• FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder
• Strategic Coalition for Data Pricing in IoT Data Markets
• Sustainable Federated Learning
• Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
• Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning
• OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
• Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
• Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent
• Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator
• Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation