联合域-类建模用于特征偏移下的联邦学习

中文摘要 AI 辅助

联邦学习（FL）能够在无需集中私有数据的情况下进行协作模型训练，但在特征偏移下性能常常下降：客户端共享标签，而条件输入分布 $p_i(x\mid y)$ 因潜在的、客户端特定的外观因素而变化。我们提出了联合域-类联邦学习（JDFL），这是一种轻量级、与优化器无关的扩展，使得这种潜在的域变化在不共享原始数据的情况下变得可用。JDFL首先从简短的本地更新信号中推断出称为伪域的域簇。然后，它将分类器头部扩展为输出 $M\times C$ 的联合（域-类）logits。这使得模型能够表示域条件外观，同时保持共享骨干网络。为了训练扩展的头部，我们引入了两种基于简单直觉的互补监督策略：一种基于相似性的软标签方法，在邻近的推断域之间传递证据，同时允许特定域的特化；以及一种每样本随机目标分配方法，扰动联合输出上的监督，并作为低成本的训练时正则化器。JDFL以最小的改动与现有的标准FL方法（如FedAvg、SCAFFOLD）集成。实验上，两种监督模式在标准域偏移图像基准上持续提高全局测试准确率；消融和敏感性研究表明，收益源于所提出的监督和参数化，而非仅仅是容量增加。

英文摘要

Federated learning (FL) enables collaborative model training without centralizing private data, but performance often degrades under feature skew: clients share labels while the conditional input distributions $p_i(x\!\mid\!y)$ vary due to latent, client-specific appearance factors. We propose Joint Domain-Class Federated Learning (JDFL), a lightweight, optimizer-agnostic extension that makes this latent domain variation usable without sharing raw data. JDFL first infers domain clusters called pseudo-domains from brief local update signals. It then expands the classifier head to output $M\times C$, joint (domain-class) logits. This allows the model to represent domain-conditioned appearance while keeping a shared backbone. To train the expanded head we introduce two complementary supervision strategies based on simple intuitions: a similarity-aware soft-labeling that transfers evidence between nearby inferred domains while allowing domain-specific specialization, and a per-sample randomized target assignment that perturbs supervision across the joint outputs and serves as a low-cost training-time regularizer. JDFL integrates with existing standard FL methods (e.g., FedAvg, SCAFFOLD) with minimal changes. Empirically, both supervision modes consistently improve global test accuracy on standard domain-shifted image benchmarks; ablations and sensitivity studies show the gains stem from the proposed supervision and parametrization rather than mere capacity increase.

发表机构

• University of Tehran（德黑兰大学）

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