  Conventional Federated Domain Adaptation (FDA) approaches usually demand an
abundance of assumptions, which makes them significantly less feasible for
real-world situations and introduces security hazards. This paper relaxes the
assumptions from previous FDAs and studies a more practical scenario named
Universal Federated Domain Adaptation (UFDA). It only requires the black-box
model and the label set information of each source domain, while the label sets
of different source domains could be inconsistent, and the target-domain label
set is totally blind. Towards a more effective solution for our newly proposed
UFDA scenario, we propose a corresponding methodology called Hot-Learning with
Contrastive Label Disambiguation (HCLD). It particularly tackles UFDA's domain
shifts and category gaps problems by using one-hot outputs from the black-box
models of various source domains. Moreover, to better distinguish the shared
and unknown classes, we further present a cluster-level strategy named
Mutual-Voting Decision (MVD) to extract robust consensus knowledge across peer
classes from both source and target domains. Extensive experiments on three
benchmark datasets demonstrate that our method achieves comparable performance
for our UFDA scenario with much fewer assumptions, compared to previous
methodologies with comprehensive additional assumptions.
