  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.
