自适应个性化联邦学习，让各设备自动学会协作与独立的平衡。
Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
- 用核均值嵌入多任务平均法，从数据中自动学出协作权重。
- 理论证明合作能降低本地风险，且在多种分布下有效。
- 通信受限时可用随机傅里叶特征，兼顾效率与节省带宽。
个性化联邦学习（PFL）使多个参与方在不共享原始数据的前提下协同训练各自模型。本文提出一种新方法：每个参与方优化所有参与方经验风险的加权组合，权重由数据自动学习，而非预先设定。其核心创新在于将协作权重估计建模为多源数据的核均值嵌入问题，借助多任务平均工具捕捉参与方间的统计关联。该方法完全自适应，无需事先了解数据异质性，可自动在全局与局部学习间切换。通过将目标重构为高维均值估计问题，我们对一大类分布给出了有限样本下本地过失风险的保证，明确量化了协作带来的统计增益。针对联邦场景中的通信限制，还提出了基于随机傅里叶特征的实用实现，可灵活权衡通信开销与统计效率。数值实验验证了理论结果。
原文摘要 · Abstract (English)
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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