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• FL-MedSegBench is the first comprehensive benchmark for federated learning on medical image segmentation.method 95%
• Personalized FL methods, particularly those with client-specific batch normalization like FedBN, consistently outperform generic FL approaches in medical image segmentation.result 90%
• No single FL method universally dominates across all datasets, with performance being dataset-dependent.result 90%
• Normalization-based personalization methods exhibit remarkable robustness to reduced communication frequency.technical 85%
• Methods like Ditto and FedRDN protect underperforming clients in federated learning for medical image segmentation.result 85%
• A method's generalization to unseen domains is strongly tied to its ability to perform well across participating clients.result 80%
• The absence of standardized benchmarks for medical image segmentation hinders fair and comprehensive evaluation of FL methods.limitation 90%
• FL-MedSegBench encompasses nine segmentation tasks across ten imaging modalities, covering both 2D and 3D formats with realistic clinical heterogeneity.method 95%

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