Personalized Federated Learning via Convex Clustering
This addresses the challenge of personalizing models in federated learning for users with heterogeneous data, but it appears incremental as it builds on existing convex clustering methods.
The paper tackles the problem of personalized federated learning by proposing a framework that uses convex clustering to automatically group users into clusters without prior knowledge, enabling personalized models for different clusters while maintaining generalization. Numerical experiments validate the approach, though no specific performance numbers are provided.
We propose a parametric family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized via a sum-of-norms penalty, weighted by a penalty parameter $λ$. The proposed approach enables "automatic" model clustering, without prior knowledge of the hidden cluster structure, nor the number of clusters. Analytical bounds on the weight parameter, that lead to simultaneous personalization, generalization and automatic model clustering are provided. The solution to the formulated problem enables personalization, by providing different models across different clusters, and generalization, by providing models different than the per-user models computed in isolation. We then provide an efficient algorithm based on the Parallel Direction Method of Multipliers (PDMM) to solve the proposed formulation in a federated server-users setting. Numerical experiments corroborate our findings. As an interesting byproduct, our results provide several generalizations to convex clustering.