Decentralized Federated Learning Framework for Social IoT With Dynamic Network Topology
Xi Liang, Jianhua Tang, Marie Siew · IEEE Internet of Things Journal · 2025
With the convergence of the social networks and the Internet of Things (IoT), social IoT (SIoT) has emerged as a promising application scenario of federated learning. Meanwhile, most centralized federated learning (CFL) algorithms encounter single-point-of-failure risks and high bandwidth pressure at the central server. Therefore, decentralized FL (DFL) has been widely studied in recent years. However, when a substantial number of social nodes participate in DFL, the model consensus process requires a significant amount of communication among social nodes. This incurs a high communication overhead and low training efficiency, especially for the SIoT with dynamic network topology. In this work, we propose a communication-effective DFL algorithm for a general dynamic SIoT network with a large number of social nodes. To improve the communication efficiency and simplify network complexity, we employ a limited label propagation algorithm (LLPA) to periodically cluster social nodes into a dynamic multi-cluster decentralized federated learning (DMC-DFL) framework. We design an effective algorithm in the formed DMC-DFL framework, which consists of three steps, i.e., local update, intra-cluster communication and inter-cluster communication. Empirically, we conduct extensive comparison and ablation experiments based on four datasets. The experiment results validate the feasibility of DMC-DFL algorithm in both static and dynamic SIoT networks and illustrate the superiority of DMC-DFL algorithm over some benchmark DFL algorithms.