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Personalized federated learning with client-specific batch normalization can dramatically outperform generic approaches in medical image segmentation, but the best method is highly dataset-dependent.
Achieve up to 20x speedup in 3D medical VLMs without sacrificing accuracy by pruning away 95% of visual tokens *without* retraining.
