Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems

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.

Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks

“…, is the secondary moment of local gradient-norm-value (GNV) of ES k. This form of GNV is widely adopted as a measure of data importance indicating the amount of information provided by the local datasets [11]. Therefore, the transformed objective function (30) can be regarded as the tradeoff between the total data importance aggregated by the CS in each round and the round latency.…”

“…Such a humongous number of UEs makes it hard for the PS to manage and monitor the training process [8,9]. Therefore, a two-layer framework is not enough, and a multi-layer FL framework emerges as a pragmatic solution for large-scale FL implementation in practice [10][11][12].…”