FedOnco-Bench: A Reproducible Benchmark for Privacy-Aware Federated Tumor Segmentation with Synthetic CT Data

Abstract--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. Results show a distinct trade-off between privacy and utility: FedA vg 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. Federated Learning (FL) [1] enables multiple clients, such as hospitals, to collaboratively train machine learning models by exchanging model parameters without sharing sensitive raw data, thereby significantly enhancing privacy. FL minimizes privacy risks inherent in traditional centralized training paradigms [1]. In oncology imaging, FL has demonstrated effectiveness; for example, Alphonse et al. reported that federated models could achieve segmentation accuracy for brain tumors comparable to centrally trained models without directly sharing MRI data [2].