  Multi-source domain adaptation has been intensively studied. The distribution
shift in features inherent to specific domains causes the negative transfer
problem, degrading a model's generality to unseen tasks. In Federated Learning
(FL), learned model parameters are shared to train a global model that
leverages the underlying knowledge across client models trained on separate
data domains. Nonetheless, the data confidentiality of FL hinders the
effectiveness of traditional domain adaptation methods that require prior
knowledge of different domain data. We propose a new federated domain
generalization method called Federated Knowledge Alignment (FedKA). FedKA
leverages feature distribution matching in a global workspace such that the
global model can learn domain-invariant client features under the constraint of
unknown client data. FedKA employs a federated voting mechanism that generates
target domain pseudo-labels based on the consensus from clients to facilitate
global model fine-tuning. We performed extensive experiments, including an
ablation study, to evaluate the effectiveness of the proposed method in both
image and text classification tasks using different model architectures. The
empirical results show that FedKA achieves performance gains of 8.8% and 3.5%
in Digit-Five and Office-Caltech10, respectively, and a gain of 0.7% in Amazon
Review with extremely limited training data. Moreover, we studied the
effectiveness of FedKA in alleviating the negative transfer of FL based on a
new criterion called Group Effect. The results show that FedKA can reduce
negative transfer, improving the performance gain via model aggregation by 4
times.
