Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
arXiv:2603.02233v1 Announce Type: new 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 quantifyi
arXiv:2603.02233v1 Announce Type: new 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.
Executive Summary
This article presents a novel approach to Personalized Federated Learning (PFL) that leverages kernel mean embedding estimation and multi-task averaging to enable adaptive collaboration among agents. The proposed method, Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings, does not require prior knowledge of data heterogeneity and can automatically transition between global and local learning regimes. The authors derive finite-sample guarantees on local excess risks and propose a practical implementation using random Fourier features to address communication constraints. Numerical experiments validate the theoretical results, demonstrating the statistical gains of collaboration.
Key Points
- ▸ The proposed method is fully adaptive and does not require prior knowledge of data heterogeneity.
- ▸ The approach leverages kernel mean embedding estimation and multi-task averaging to capture statistical relationships between agents.
- ▸ Finite-sample guarantees on local excess risks are derived for a broad class of distributions.
Merits
Strength in Theoretical Contributions
The article makes significant contributions to the theoretical foundations of PFL by deriving finite-sample guarantees on local excess risks.
Strength in Practical Implementation
The proposed implementation using random Fourier features effectively addresses communication constraints inherent to federated settings.
Demerits
Limitation in Computational Complexity
The computational complexity of the proposed method may be high due to the use of kernel mean embedding estimation and multi-task averaging.
Limitation in Scalability
The method may not be scalable to very large numbers of agents due to the need for pairwise comparisons of kernel mean embeddings.
Expert Commentary
The article presents a well-motivated and theoretically sound approach to PFL. The use of kernel mean embedding estimation and multi-task averaging is a novel and innovative aspect of the method. The finite-sample guarantees on local excess risks are a significant contribution to the field. However, the computational complexity and scalability of the proposed method are areas that require further investigation. The article has significant implications for both practical applications and policy development, highlighting the importance of adaptive collaboration in PFL.
Recommendations
- ✓ Future research should focus on developing more efficient and scalable implementations of the proposed method.
- ✓ The use of more advanced optimization techniques, such as stochastic gradient descent, may help to reduce the computational complexity of the method.