2022/02/01 by Aleksandar Armacki, Dragana Bajovic, Armacki, Aleksandar +7 · 2 citations
Computer Science · #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #Recommender Systems and Techniques #cs.LG
paper · pdf · doi:10.48550/arxiv.2202.00718
Changed template. Figure 4 separated into Figure 4 and Figure 5
openalex publication_date 2022/02/01 · arxiv created 2022/02/18 · arxiv updated 2022/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.