  The rapid development of multimedia has provided a large amount of data with
different distributions for visual tasks, forming different domains. Federated
Learning (FL) can efficiently use this diverse data distributed on different
client media in a decentralized manner through model sharing. However, in
open-world scenarios, there is a challenge: global models may struggle to
predict well on entirely new domain data captured by certain media, which were
not encountered during training. Existing methods still rely on strong
statistical correlations between samples and labels to address this issue,
which can be misleading, as some features may establish spurious short-cut
correlations with the predictions. To comprehensively address this challenge,
we introduce FedCD (Cross-Domain Invariant Federated Learning), an overall
optimization framework at both the local and global levels. We introduce the
Spurious Correlation Intervener (SCI), which employs invariance theory to
locally generate interventers for features in a self-supervised manner to
reduce the model's susceptibility to spurious correlated features. Our approach
requires no sharing of data or features, only the gradients related to the
model. Additionally, we develop the simple yet effective Risk Extrapolation
Aggregation strategy (REA), determining aggregation coefficients through
mathematical optimization to facilitate global causal invariant predictions.
Extensive experiments and ablation studies highlight the effectiveness of our
approach. In both classification and object detection generalization tasks, our
method outperforms the baselines by an average of at least 1.45% in Acc, 4.8%
and 1.27% in mAP50.
