In this article, we propose an approach for federated domain adaptation, a setting where distributional shift exists among clients and some have unlabeled data. The proposed framework, FedDaDiL, tackles the resulting challenge through dictionary learning of empirical distributions. In our setting, clients' distributions represent particular domains, and FedDaDiL collectively trains a federated dictionary of empirical distributions. In particular, we build upon the Dataset Dictionary Learning framework by designing collaborative communication protocols and aggregation operations. The chosen protocols keep clients' data private, thus enhancing overall privacy compared to its centralized counterpart. We empirically demonstrate that our approach successfully generates labeled data on the target domain with extensive experiments on (i) Caltech-Office, (ii) TEP, and (iii) CWRU benchmarks. Furthermore, we compare our method to its centralized counterpart and other benchmarks in federated domain adaptation.

翻译：本文提出了一种联邦域适应方法，适用于客户端之间存在分布偏移且部分客户端拥有未标注数据的场景。所提出的FedDaDiL框架通过经验分布的字典学习解决这一挑战。在我们的设定中，客户端分布代表特定领域，FedDaDiL协同训练一个经验分布的联邦字典。具体而言，我们基于数据集字典学习框架，设计了协作通信协议与聚合操作。所选协议能够保护客户数据隐私，相比集中式方法显著提升了隐私安全性。通过在（i）Caltech-Office、（ii）TEP和（iii）CWRU基准上的大量实验，我们实证证明了该方法能有效在目标域生成标注数据。此外，我们将该方法与集中式对应方法及其他联邦域适应基准进行了比较。