让下一篇重要论文，不再错过。

原文摘要

Federated learning lets hospitals train segmentation models without sharing patient data, but standard evaluation reports one number: mean Dice across all sites. That number can look competitive while one hospital runs far behind the rest. Our results suggest this gap is not primarily a federation problem. Under controlled MRI appearance shift on BraTS 2020, even a centralised model trained on all data (single seed) shows a 0.055 best-worst client gap. Under FedAvg, federation adds a further 0.030 on top of that. In our setup, appearance shift accounts for most of the gap and federation widens it. We test five methods - centralised, FedAvg, FedProx, FedBN, and q-FedAvg - under this protocol. q-FedAvg achieves the best worst-client Dice in both full-data and subsampled regimes while also maintaining the highest mean Dice among FL methods: no accuracy-fairness tradeoff in our setting, though its margin over FedAvg is not significant at five seeds (p=0.436). FedProx significantly lowers the worst client across five seeds (p=0.019). Worst-client rankings are identical across both evaluation regimes, but FedBN's worst-client spread across seeds is nearly double FedAvg's - a reliability gap that single-seed evaluation cannot see. MedFL-Stress packages this stress test as a reproducible protocol. The main practical message: report worst-client Dice and confirm rankings across evaluation regimes before drawing conclusions from any FL benchmark.