BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction

Collin Meese, Hang Chen, Syed Ali Asif, Wanxin Li, Chien Chung Shen, Mark Nejad

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

7 Citations (Scopus)

Abstract

Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from privacy concerns due to the sensitive nature of transportation data. Moreover, the emerging literature on traffic prediction by distributed learning approaches, including federated learning, primarily focuses on offline learning. This paper proposes BFRT, a blockchained federated learning architecture for online traffic flow prediction using real-time data and edge computing. The proposed approach provides privacy for the underlying data, while enabling decentralized model training in real-time at the Internet of Vehicles edge. We federate GRU and LSTM models and conduct extensive experiments with dynamically collected arterial traffic data shards. We prototype the proposed permissioned blockchain network on Hyperledger Fabric and perform extensive tests using virtual machines to simulate the edge nodes. Experimental results outperform the centralized models, highlighting the feasibility of our approach for facili-tating privacy-preserving and decentralized real-time traffic flow prediction.

Original languageEnglish
Title of host publicationProceedings - 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages317-326
Number of pages10
ISBN (Electronic)9781665499569
DOIs
Publication statusPublished - 2022
Event22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022 - Taormina, Italy
Duration: 16 May 202219 May 2022

Publication series

NameProceedings - 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022

Conference

Conference22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022
Country/TerritoryItaly
CityTaormina
Period16/05/2219/05/22

Keywords

  • Blockchain
  • Federated Learning
  • Traffic Flow Prediction

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