Federated Fuzzy C-Means for Multilayer Network Community Detection in Industrial Internet of Things

Multi-layer network community detection is a crucial topic in Industrial Internet of things(IIoT). Due to communication and privacy requirements, network data is distributed across multiple devices, being a significant challenge to develop a model to learn latent information for community detection. To address the problem, this paper proposes a federated fuzzy C-Means for multi-layer network community detection. Firstly, non-negative matrix factorization is employed to obtain a low-dimensional representation via training local data in each client. The gradients of the global centroids are then transmitted to a central server for consistent fusion and complete community detection within the fuzzy C-Means framework. As a result, the training process for each client remains independent and leads to effectively privacy preservation. Experimental results demonstrate that the proposed method can successfully perform multi-layer network community detection across distributed devices and achieve comparable performance in contrast with centralized community detection methods on four public datasets.