The most recent multi-source covariate shift algorithm is an efficient hyperparameter optimization algorithm for missing target output. In this paper, we extend this algorithm to the framework of federated learning. For data islands in federated learning and covariate shift adaptation, we propose the federated domain adaptation estimate of the target risk which is asymptotically unbiased with a desirable asymptotic variance property. We construct a weighted model for the target task and propose the federated covariate shift adaptation algorithm which works preferably in our setting. The efficacy of our method is justified both theoretically and empirically.

翻译：最新的多源协变量偏移算法是一种针对缺失目标输出的高效超参数优化算法。本文将该算法扩展至联邦学习框架。针对联邦学习中的数据孤岛与协变量偏移适应问题，我们提出了目标风险的联邦域自适应估计量，该估计量具有渐近无偏性及理想的渐近方差性质。通过构建目标任务的加权模型，我们提出了适用于本场景的联邦协变量偏移自适应算法。从理论分析和实验验证两方面证明了该方法的有效性。