构建合成肿瘤CT数据的联邦分割基准，评估隐私与精度权衡。
FedOnco-Bench: A Reproducible Benchmark for Privacy-Aware Federated Tumor Segmentation with Synthetic CT Data
- 用合成肿瘤CT数据构建可复现的联邦学习基准
- DP-SGD隐私更强但精度下降，传统方法精度高但易泄露
- 适合医疗图像隐私保护研究者和联邦学习算法开发者
联邦学习（FL）可在保护数据隐私的前提下实现多机构协作建模，适用于敏感医疗场景。然而现有系统仍面临成员推断攻击和数据异质性问题。本文提出FedOnco-Bench，一个基于合成肿瘤增强CT影像的可复现联邦肿瘤分割基准。评估了四种主流联邦方法：FedAvg、FedProx、FedBN及引入差分隐私的DP-SGD。结果表明存在明显隐私-性能权衡：FedAvg性能最优（Dice约0.85），但隐私泄露严重（攻击AUC约0.72）；而DP-SGD显著提升隐私保护（AUC约0.25），但精度下降至Dice约0.79。在非同分布客户端数据下，FedProx与FedBN表现更均衡。该基准为医学图像分割中的隐私保护联邦学习提供了标准化开源平台。
原文摘要 · Abstract (English)
Federated Learning (FL) allows multiple institutions to cooperatively train machine learning models while retaining sensitive data at the source, which has great utility in privacy-sensitive environments. However, FL systems remain vulnerable to membership-inference attacks and data heterogeneity. This paper presents FedOnco-Bench, a reproducible benchmark for privacy-aware FL using synthetic oncologic CT scans with tumor annotations. It evaluates segmentation performance and privacy leakage across FL methods: FedAvg, FedProx, FedBN, and FedAvg with DP-SGD. Results show a distinct trade-off between privacy and utility: FedAvg is high performance (Dice around 0.85) with more privacy leakage (attack AUC about 0.72), while DP-SGD provides a higher level of privacy (AUC around 0.25) at the cost of accuracy (Dice about 0.79). FedProx and FedBN offer balanced performance under heterogeneous data, especially with non-identical distributed client data. FedOnco-Bench serves as a standardized, open-source platform for benchmarking and developing privacy-preserving FL methods for medical image segmentation.
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