Jigsaw Game: Federated Clustering
<|reference_start|>Jigsaw Game: Federated Clustering: 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.<|reference_end|>
@article{xu2024jigsaw,
    title={Jigsaw Game: Federated Clustering},
    author={Jinxuan Xu, Hong-You Chen, Wei-Lun Chao, Yuqian Zhang},
    journal={arXiv preprint arXiv:2407.12764},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12764},
    primaryClass={cs.LG}
}