Federated Learning (FL) facilitates distributed model development to
aggregate multiple confidential data sources. The information transfer among
clients can be compromised by distributional differences, i.e., by non-i.i.d.
data. A particularly challenging scenario is the federated model adaptation to
a target client without access to annotated data. We propose Federated
Adversarial Cross Training (FACT), which uses the implicit domain differences
between source clients to identify domain shifts in the target domain. In each
round of FL, FACT cross initializes a pair of source clients to generate domain
specialized representations which are then used as a direct adversary to learn
a domain invariant data representation. We empirically show that FACT
outperforms state-of-the-art federated, non-federated and source-free domain
adaptation models on three popular multi-source-single-target benchmarks, and
state-of-the-art Unsupervised Domain Adaptation (UDA) models on
single-source-single-target experiments. We further study FACT's behavior with
respect to communication restrictions and the number of participating clients.

本文提出了 Federated Adversarial Cross Training （FACT）方法来解决非 i.i.d. 数据的情况下，联邦模型适应目标客户端的挑战。通过使用不同领域的源客户端之间的差异来识别目标域中的域漂移，该方法在多源单目标基准测试中优于现有的联邦模型、非联邦模型和单源单目标实验中的最新无监督域自适应模型。