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Title	Differentially Private Traffic Flow Prediction Using Transformers: A Federated Approach
ID_Doc	19912
Authors	Gupta S.; Torra V.
Year	2024
Published	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14398 LNCS
DOI	http://dx.doi.org/10.1007/978-3-031-54204-6_15
Abstract	Accurate traffic flow prediction plays an important role in intelligent transportation management and reducing traffic congestion for smart cities. Existing traffic flow prediction techniques using deep learning, mostly LSTMs, have achieved enormous success based on the large traffic flow datasets collected by governments and different organizations. Nevertheless, a lot of these datasets contain sensitive attributes that may relate to users’ private data. Hence, there is a need to develop an accurate traffic flow prediction mechanism that preserves users’ privacy. To address this challenge, we propose a federated learning-based temporal fusion transformer framework for traffic flow prediction which is a distributed machine learning approach where all the model updates are aggregated through an aggregation algorithm rather than sharing and storing the raw data in one centralized location. The proposed framework trains the data locally on client devices using temporal fusion transformers and differential privacy. Experiments show that the proposed framework can guarantee accuracy in predicting traffic flow for both the short and long term. © The Author(s) 2024.
Author Keywords	Differential Privacy; Federated Learning; Temporal Fusion Transformer; Time Series Data; Traffic Flow Prediction

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