  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.
