Modern machine learning (ML) models have grown to a scale where training them
on a single machine becomes impractical. As a result, there is a growing trend
to leverage federated learning (FL) techniques to train large ML models in a
distributed and collaborative manner. These models, however, when deployed on
new devices, might struggle to generalize well due to domain shifts. In this
context, federated domain adaptation (FDA) emerges as a powerful approach to
address this challenge.
Most existing FDA approaches typically focus on aligning the distributions
between source and target domains by minimizing their (e.g., MMD) distance.
Such strategies, however, inevitably introduce high communication overheads and
can be highly sensitive to network reliability.
In this paper, we introduce RF-TCA, an enhancement to the standard Transfer
Component Analysis approach that significantly accelerates computation without
compromising theoretical and empirical performance. Leveraging the
computational advantage of RF-TCA, we further extend it to FDA setting with
FedRF-TCA. The proposed FedRF-TCA protocol boasts communication complexity that
is \emph{independent} of the sample size, while maintaining performance that is
either comparable to or even surpasses state-of-the-art FDA methods. We present
extensive experiments to showcase the superior performance and robustness (to
network condition) of FedRF-TCA.

在现代机器学习模型中，单机上训练这些规模的模型往往变得不现实，因此越来越多的人开始借用联邦学习技术以分布式和协作的方式来训练大规模机器学习模型。然而，当这些模型在新设备上部署时，可能因为领域转移而难以泛化。本文中，我们介绍了 RF-TCA，这是对标准的转移组件分析方法的改进，它在不损害理论和实证性能的情况下显著加速了计算过程。利用 RF-TCA 的计算优势，我们进一步将其扩展到联邦领域适应性设置中的 FedRF-TCA。所提出的 FedRF-TCA 协议在通信复杂度上与样本大小无关，同时保持着与最先进的联邦领域适应性方法相当甚至超过其性能。我们进行了大量实验来展示 FedRF-TCA 的卓越性能和对网络条件的稳健性。