Conventional Federated Domain Adaptation (FDA) approaches usually demand an abundance of assumptions, such as label set consistency, which makes them significantly less feasible for real-world situations and introduces security hazards. In this work, we propose 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. This relaxes the assumptions made by FDA, which are often challenging to meet in real-world cases and diminish model security. To address the UFDA scenario, we propose a corresponding framework called Hot-Learning with Contrastive Label Disambiguation (HCLD), which tackles UFDA's domain shifts and category gaps problem 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. The extensive experiments on three benchmarks demonstrate that our HCLD achieves comparable performance for our UFDA scenario with much fewer assumptions, compared to the previous methodologies with many additional assumptions.

我们提出了一种更实用的方案，名为Hot-Learning with Contrastive Label Disambiguation（HCLD），解决了Universal Federated Domain Adaptation（UFDA）场景中的领域变化和类别差距问题，并通过使用各个源域的黑盒模型的单热输出来区分共享类和未知类。在三个基准测试上的广泛实验表明，与具有许多额外假设的先前方法相比，我们的HCLD在UFDA场景中以更少的假设达到了可比较的性能。

UFDA：具有实际假设的通用联邦域适应