FedDDL improves federated out-of-distribution generalization by generating background-mixed counterfactual samples and aligning clients with causal prototypes, yielding an average Top-1 gain of about 4.5 percent over nine baselines.
Multi-source domain adaptation for visual sentiment classification
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Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization
FedDDL improves federated out-of-distribution generalization by generating background-mixed counterfactual samples and aligning clients with causal prototypes, yielding an average Top-1 gain of about 4.5 percent over nine baselines.