  Domain generalization (DG) aims to learn from multiple known source domains a
model that can generalize well to unknown target domains. The existing DG
methods usually exploit the fusion of shared multi-source data to train a
generalizable model. However, tremendous data is distributed across lots of
places nowadays that can not be shared due to privacy policies. In this paper,
we tackle the problem of federated domain generalization where the source
datasets can only be accessed and learned locally for privacy protection. We
propose a novel framework called Collaborative Semantic Aggregation and
Calibration (CSAC) to enable this challenging problem. To fully absorb
multi-source semantic information while avoiding unsafe data fusion, we conduct
data-free semantic aggregation by fusing the models trained on the separated
domains layer-by-layer. To address the semantic dislocation problem caused by
domain shift, we further design cross-layer semantic calibration with an
attention mechanism to align each semantic level and enhance domain invariance.
We unify multi-source semantic learning and alignment in a collaborative way by
repeating the semantic aggregation and calibration alternately, keeping each
dataset localized, and the data privacy is carefully protected. Extensive
experiments show the significant performance of our method in addressing this
challenging problem.
