Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems
This work addresses communication-efficiency and heterogeneity issues for automated vehicles and road infrastructures in C-ITS, representing an incremental improvement over existing federated learning methods.
The paper tackles the challenge of large data volumes in Cooperative Intelligent Transport Systems (C-ITS) by proposing a Semi-asynchronous Hierarchical Federated Learning (SHFL) framework, which achieves a tradeoff between training accuracy and transmission latency with demonstrated advantages in training overhead and model performance.
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.