FMDA-OT: Federated Multi-source Domain Adaptation Through Optimal
  Transport
Omar Ghannou Younès Bennani
Multi-source Domain Adaptation (MDA) aims to adapt models trained on multiple
labeled source domains to an unlabeled target domain. In this paper, we
introduce our approach as a collaborative MDA framework, which comprises two
adaptation phases. Firstly, we conduct domain adaptation for each source
individually with the target, utilizing optimal transport. Then, in the second
phase, which constitutes the final part of the framework, we design the
architecture of centralized federated learning to collaborate the N models
representing the N sources. This architecture offers the advantage of using the
sources without accessing their data, thus resolving data privacy issues
inherent in domain adaptation. Additionally, during this phase, the server
guides and fine-tunes the adaptation using a small number of pseudo-labeled
samples available in the target domain, referred to as the target validation
subset of the dataset.