Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings

• URL: http://arxiv.org/abs/2603.02233v1
• Date: Fri, 13 Feb 2026 07:49:03 GMT
• Title: Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
• Abstract summary: Federated learning enables a collection of agents to collaboratively learn individual models without sharing raw data.<n>We propose a new PFL approach in which each agent optimize a weighted combination of all agents' empirical risks, with the weights learned from data rather than specified a priori.
• Score: 16.820009380421368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized Federated Learning (PFL) enables a collection of agents to collaboratively learn individual models without sharing raw data. We propose a new PFL approach in which each agent optimizes a weighted combination of all agents' empirical risks, with the weights learned from data rather than specified a priori. The novelty of our method lies in formulating the estimation of these collaborative weights as a kernel mean embedding estimation problem with multiple data sources, leveraging tools from multi-task averaging to capture statistical relationships between agents. This perspective yields a fully adaptive procedure that requires no prior knowledge of data heterogeneity and can automatically transition between global and local learning regimes. By recasting the objective as a high-dimensional mean estimation problem, we derive finite-sample guarantees on local excess risks for a broad class of distributions, explicitly quantifying the statistical gains of collaboration. To address communication constraints inherent to federated settings, we also propose a practical implementation based on random Fourier features, which allows one to trade communication cost for statistical efficiency. Numerical experiments validate our theoretical results.

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