Heterogeneity across devices in federated learning (FL) typically refers to statistical (e.g., non-i.i.d. data distributions) and resource (e.g., communication bandwidth) dimensions. In this paper, we focus on another important dimension that has received less attention: varying quantities/distributions of labeled and unlabeled data across devices. In order to leverage all data, we develop a decentralized federated domain adaptation methodology which considers the transfer of ML models from devices with high quality labeled data (called sources) to devices with low quality or unlabeled data (called targets). Our methodology, Source-Target Determination and Link Formation (ST-LF), optimizes both (i) classification of devices into sources and targets and (ii) source-target link formation, in a manner that considers the trade-off between ML model accuracy and communication energy efficiency. To obtain a concrete objective function, we derive a measurable generalization error bound that accounts for estimates of source-target hypothesis deviations and divergences between data distributions. The resulting optimization problem is a mixed-integer signomial program, a class of NP-hard problems, for which we develop an algorithm based on successive convex approximations to solve it tractably. Subsequent numerical evaluations of ST-LF demonstrate that it improves classification accuracy and energy efficiency over state-of-the-art baselines.

本文介绍了一种去中心化的联邦域自适应方法（ST-LF），旨在通过将机器学习模型从高质量标记数据的设备传输到低质量或未标记的数据设备来最大限度地利用所有数据。该方法考虑到设备间带宽的限制，通过优化设备的分类和源目标之间的链接形成来实现对通信能量效率和精度之间的权衡。通过数值评估，证明了该方法在精度和能量效率方面都优于现有技术基线。

多源到多目标去中心化联邦域自适应