Jigsaw Game: Federated Clustering
This addresses the underexplored challenge of clustering in federated settings, which is incremental as it adapts existing methods to a new context.
The paper tackles the problem of unsupervised clustering in federated learning, specifically federated k-means, by proposing a one-shot algorithm called FeCA that aggregates refined local solutions to recover the global solution in a single round, demonstrating robustness on synthetic and real-world data.
Federated learning has recently garnered significant attention, especially within the domain of supervised learning. However, despite the abundance of unlabeled data on end-users, unsupervised learning problems such as clustering in the federated setting remain underexplored. In this paper, we investigate the federated clustering problem, with a focus on federated k-means. We outline the challenge posed by its non-convex objective and data heterogeneity in the federated framework. To tackle these challenges, we adopt a new perspective by studying the structures of local solutions in k-means and propose a one-shot algorithm called FeCA (Federated Centroid Aggregation). FeCA adaptively refines local solutions on clients, then aggregates these refined solutions to recover the global solution of the entire dataset in a single round. We empirically demonstrate the robustness of FeCA under various federated scenarios on both synthetic and real-world data. Additionally, we extend FeCA to representation learning and present DeepFeCA, which combines DeepCluster and FeCA for unsupervised feature learning in the federated setting.