Robust and Communication-Efficient Federated Domain Adaptation via
  Random Features
Zhanbo Feng Yuanjie Wang Jie Li Fan Yang Jiong Lou Tiebin Mi Robert. C. Qiu Zhenyu Liao
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.