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：本文提出了一种面向联邦域适应的方法，该场景下各客户端之间存在分布偏移，且部分客户端拥有无标签数据。所提出的框架FedDaDiL通过经验分布字典学习应对这一挑战。在我们的设定中，客户端分布代表特定领域，FedDaDiL通过联合训练形成一个联邦经验分布字典。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。所选取的协议能够保护客户端数据的隐私性，相较于集中式方案显著提升了隐私保护能力。通过在（i）Caltech-Office、（ii）TEP和（iii）CWRU基准测试上的大量实验，我们实证证明了该方法能够在目标域上成功生成带标签数据。此外，我们还将本方法与集中式对应方案及联邦域适应领域的其他基准方法进行了比较。