Fair Dataset Distillation via Cross-Group Barycenter Alignment

ArXi:2605.00185v1 Announce Type: cross Dataset Distillation aims to compress a large dataset into a small synthetic one while maintaining predictive performance. We show that as different graphic groups exhibit distinct predictive patterns, the distillation process struggles to simultaneously preserve informative signals for all subgroups, regardless of whether group sizes are mildly or severely imbalanced. Consequently, models trained on distilled data can experience substantial performance drops for certain subgroups, leading to fairness gaps.