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.

该论文提出了一种应对联邦领域自适应的方法，通过字典学习来处理客户端之间存在的分布转移和部分无标签数据的情况。该方法使用经验分布的字典来训练联邦模型，采用协作通信协议和聚合操作来保护客户端数据的隐私，并通过实验证明其在目标领域上成功生成标记数据，并与其集中式对应物和其他基准进行了比较。

多源域自适应的联邦数据集字典学习