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Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network

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Abstract

Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models. However, data privacy and security are always a challenge in every field where data need to be uploaded to the cloud. Federated learning (FL) is an emerging trend for distributed training of data. The primary goal of FL is to train an efficient communication model without compromising data privacy. The traffic data have a robust spatio-temporal correlation, but various approaches proposed earlier have not considered spatial correlation of the traffic data. This paper presents FL-based traffic flow prediction with spatio-temporal correlation. This work uses a differential privacy (DP) scheme for privacy preservation of participant’s data. To the best of our knowledge, this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation. The proposed framework trains the data locally at the client-side with DP. It then uses the model aggregation mechanism federated graph convolutional network (FedGCN) at the server-side to find the average of locally trained models. The results of the proposed work show that the FedGCN model accurately predicts the traffic. DP scheme at client-side helps clients to set a budget for privacy loss.

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Authors and Affiliations

  1. Pimpri Chinchwad College of Engineering, Pune, 411044, India

    Savita Lonare

  2. Department of Computer Science and Engineering, GITAM, Viskhapatnam, 530045, India

    Savita Lonare & Ravi Bhramaramba

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  1. Savita Lonare
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  2. Ravi Bhramaramba
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Correspondence to Savita Lonare.

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Lonare, S., Bhramaramba, R. Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network. J. Shanghai Jiaotong Univ. (Sci.) (2021). https://doi.org/10.1007/s12204-021-2382-5

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  • DOI: https://doi.org/10.1007/s12204-021-2382-5

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