  Due to privacy, storage, and other constraints, there is a growing need for
unsupervised domain adaptation techniques in machine learning that do not
require access to the data used to train a collection of source models.
Existing methods for multi-source-free domain adaptation (MSFDA) typically
train a target model using pseudo-labeled data produced by the source models,
which focus on improving the pseudo-labeling techniques or proposing new
training objectives. Instead, we aim to analyze the fundamental limits of
MSFDA. In particular, we develop an information-theoretic bound on the
generalization error of the resulting target model, which illustrates an
inherent bias-variance trade-off. We then provide insights on how to balance
this trade-off from three perspectives, including domain aggregation, selective
pseudo-labeling, and joint feature alignment, which leads to the design of
novel algorithms. Experiments on multiple datasets validate our theoretical
analysis and demonstrate the state-of-art performance of the proposed
algorithm, especially on some of the most challenging datasets, including
Office-Home and DomainNet.
