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Existing image-to-image evaluations miss a critical aspect: whether the output image actually preserves the content of the input.
Domain-aware federated learning: By creating separate prototype clusters for each domain, FedDAP significantly improves performance in heterogeneous federated learning scenarios where clients have different data distributions.
