联邦学习下提升模型跨域泛化能力，兼顾隐私与效率
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
- 通过跨客户端特征扩展增强本地数据多样性
- 双阶段对齐机制有效提取领域不变特征，提升泛化性能
- 轻量设计适合实际联邦场景，代码开源可复现
领域泛化旨在训练能有效适应未见领域的模型。然而在联邦学习中，各客户端协同训练却不共享数据，现有多数领域泛化算法因隐私限制、单客户端数据量少及领域多样性不足而难以适用。为此，本文提出 FedCCRL，一种轻量级联邦领域泛化方法，在保障隐私的同时显著提升模型泛化能力，并确保计算与通信高效。该方法包含两个核心模块：一是跨客户端特征扩展模块，通过跨客户端领域迁移和领域不变特征扰动增加本地领域多样性；二是表示与预测双阶段对齐模块，使模型能有效捕捉领域不变特征。大量实验表明，FedCCRL 在 PACS、OfficeHome 与 miniDomainNet 数据集上，于不同客户端数量的联邦设置下均达到当前最优性能。代码已开源。
原文摘要 · Abstract (English)
Domain Generalization (DG) aims to train models that can effectively generalize to unseen domains. However, in the context of Federated Learning (FL), where clients collaboratively train a model without directly sharing their data, most existing DG algorithms are not directly applicable to the FL setting due to privacy constraints, as well as the limited data quantity and domain diversity at each client. To tackle these challenges, we propose FedCCRL, a lightweight federated domain generalization method that significantly improves the model's generalization ability while preserving privacy and ensuring computational and communication efficiency. Specifically, FedCCRL comprises two principal modules: the first is a cross-client feature extension module, which increases local domain diversity via cross-client domain transfer and domain-invariant feature perturbation; the second is a representation and prediction dual-stage alignment module, which enables the model to effectively capture domain-invariant features. Extensive experimental results demonstrate that FedCCRL achieves the state-of-the-art performance on the PACS, OfficeHome and miniDomainNet datasets across FL settings of varying numbers of clients. Code is available at https://github.com/sanphouwang/fedccrl
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