Abstract
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 (FL). Meanwhile, most centralized FL (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 multicluster DFL (DMC-DFL) framework. We design an effective algorithm in the formed DMC-DFL framework, which consists of three steps, i.e., local update, intracluster communication, and intercluster 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.