Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation

Understand

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.