Unsupervised Federated Learning Based on Evolving Gaussian Clustering

In this paper, we propose an unsupervised federated learning approach for evolving data stream clustering. One of the main challenges in federated clustering is selecting the number of clusters, as the data cannot be examined directly, and each client may have a distinct number of data clusters. Furthermore, data distributions in many real-world systems are not static but evolve over time due to changing environmental conditions, shifting processes, or behavioral patterns. To address these challenges, an Evolving Federated Gaussian Clustering (eFedG) method is proposed that adds and merges clusters over time, without assuming a predefined number of clusters. We propose a methodology for incremental clustering from mini-batches, with a merging mechanism that processes multiple cluster pairs simultaneously in a single step. This approach enables the system to handle heterogeneous data, as local clusters are learned independently and aggregated at the server based on overlap. The federated clustering method was examined on synthetic toy datasets, federated streaming clustering, and real network intrusion data.