Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for vision tasks. Unlike typical federated settings with labeled client data, we consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data). Within this new Federated Multi-Target Domain Adaptation (FMTDA) task, we analyze the model performance of exiting domain adaptation methods and propose an effective DualAdapt method to address the new challenges. Extensive experimental results on image classification and semantic segmentation tasks demonstrate that our method achieves high accuracy, incurs minimal communication cost, and requires low computational resources on client devices.

提出一种新的基于联邦学习的多目标领域自适应算法，通过对模型性能的分析，发现现有领域自适应方法在面对该问题时存在的问题，并提出一种有效的 DualAdapt 方法来解决这些问题。在图像分类和语义分割任务上的大量实验结果表明，我们的方法在客户端设备上实现高准确度，最小通信成本和所需计算资源低。

联邦多目标域自适应