Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems

• URL: http://arxiv.org/abs/2110.09073v1
• Date: Mon, 18 Oct 2021 07:44:34 GMT
• Title: Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems
• Authors: Qimei Chen and Zehua You and Hao Jiang
• Abstract summary: Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures. The components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. We propose a novel Semi-a synchronous Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing.
• Score: 10.257042901204528
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures by taking advantages from participants. However, the components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. Currently, federated learning has been proposed as an appealing approach to allow users to cooperatively reap the benefits from trained participants. Therefore, in this paper, we propose a novel Semi-asynchronous Hierarchical Federated Learning (SHFL) framework for C-ITS that enables elastic edge to cloud model aggregation from data sensing. We further formulate a joint edge node association and resource allocation problem under the proposed SHFL framework to prevent personalities of heterogeneous road vehicles and achieve communication-efficiency. To deal with our proposed Mixed integer nonlinear programming (MINLP) problem, we introduce a distributed Alternating Direction Method of Multipliers (ADMM)-Block Coordinate Update (BCU) algorithm. With this algorithm, a tradeoff between training accuracy and transmission latency has been derived. Numerical results demonstrate the advantages of the proposed algorithm in terms of training overhead and model performance.

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