A Spatiotemporal Coupling-Based Clustered Federated Learning Scheme for Low Latency Digital Twin Within Heterogeneous IIoT

Abstract

Data-driven deep learning (DL) techniques have increasingly been employed to construct digital twin (DT) models for the intelligent industrial Internet of Things (IIoT) systems. Within DTs, federated learning (FL) offers a decentralized framework that enables distributed entities to collaboratively update global models without sharing raw data. Clustered FL (CFL) further enhances training efficiency by grouping clients, thereby reducing communication overhead and accelerating model convergence. However, data heterogeneity arising from spatial distribution differences and system heterogeneity, resulting in straggler clients, jointly hinder the convergence and efficiency of CFL. The interplay of these factors introduces spatiotemporal coupling, which further degrades model training. To address these challenges, we propose a spatiotemporal coupling-based CFL scheme that jointly optimizes client clustering and aggregation strategies to minimize overall training latency. A semisynchronous aggregation mechanism is introduced, allowing clients to update at different frequencies based on their delay tiers. Furthermore, client clustering is performed according to location similarity to improve convergence, while clients with higher delay tiers and greater data diversity are prioritized for cluster head selection. To mitigate the impact of imbalanced cluster sizes under data heterogeneity, a balanced matching optimization is formulated to evenly distribute remaining clients to the nearest cluster heads. Within each cluster, adaptive bandwidth allocation is employed to satisfy delay-tier constraints and shorten communication rounds. Extensive simulations on CIFAR-10 and Fashion-MNIST with nonindependent and identically distributed settings show that the proposed scheme can reduce the total training latency by up to 38.71% and 8.87%, respectively, to reach a fixed target accuracy, while achieving comparable model accuracy to existing baselines. These results confirm the effectiveness of the proposed scheme in heterogeneous IIoT environments.