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 novel federated domain generalization method that significantly improves the model's ability to generalize to unseen domains without compromising privacy or incurring excessive computational and communication costs. Specifically, we adapt MixStyle to the federated setting to transfer domain-specific features while AugMix is employed to perturb domain-invariant features. Furthermore, we leverage supervised contrastive loss for representation alignment and utilize Jensen-Shannon divergence to ensure consistent predictions between original and augmented samples. Extensive experimental results demonstrate that FedCCRL achieves the state-of-the-art performances on the PACS, OfficeHome and miniDomainNet datasets across varying numbers of clients. Code is available at https://github.com/SanphouWang/FedCCRL.

本研究针对联邦学习中领域泛化的挑战，提出了FedCCRL方法，以实现模型在未见领域的有效泛化，同时保护隐私并降低计算和通信成本。通过将MixStyle适配于联邦设置，结合AugMix和对比损失，本方法在PACS、OfficeHome和miniDomainNet数据集上取得了先进的性能。

FedCCRL: 具有跨客户端表示学习的联邦领域泛化