The paper introduces XCal-FL, a federated learning algorithm that dynamically calibrates differential privacy noise using three signals—prediction logit variations, counterfactual margins, and saliency concentration—to improve both predictive accuracy and explanation fidelity. Experiments on medical imaging datasets demonstrate that XCal-FL outperforms static-noise and state‑of‑the‑art adaptive DP methods, achieving over 10% better accuracy and up to five‑fold higher explanation fidelity while using privacy budgets more efficiently. The study reveals that explanation fidelity behaves non‑linearly with privacy loss, indicating that explainability is a separate dimension of the privacy trade‑off that cannot be inferred from utility alone.
The paper evaluates privacy‑aware federated learning for breast cancer imaging by comparing pretrained vision‑language models (CLIP, BiomedCLIP) and a ViT‑B/16 baseline on two public datasets, with and without differential privacy. All five models achieve high normal‑versus‑sick discrimination (ROC‑AUC 0.940‑0.990), but acquisition year alone attains 0.974, indicating potential confounding. Removing the text pathway keeps accuracy while cutting communication cost, while differential privacy lowers ROC‑AUC by 0.375 due to perturbing the full model state.
By Malithi Mithsara Wanniarachchi Kankanamge, Ning Yang, Sree Bash Debnath, Md Zobaer Islam, Abdur R. Shahid
arXiv:2610.27470v1 Announce Type: cross
Abstract: Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions,...
By Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea
Fed-IGTD is a federated learning framework that enables tabular-to-image encoding without centralizing data. It reconstructs a global correlation matrix from local statistics and trains a CNN via Federated Averaging, while applying two differential privacy mechanisms: a one-shot Gaussian noise addition during correlation aggregation and a client‑level DP‑FedAvg during training. Experiments on five datasets show that privacy protection during mapping construction has minimal impact on performance, whereas DP‑FedAvg can significantly degrade utility, especially with small client cohorts and moderate privacy budgets.
arXiv:2601. 19618v2 Announce Type: replace-cross Abstract: Differential privacy protects the patients whose images train medical imaging models, but it lowers diagnostic accuracy, and the initialization is the strongest known remedy.
By Soroosh Tayebi Arasteh, Mina Farajiamiri, Mahshad Lotfinia, Behrus Hinrichs-Puladi, Jonas Bienzeisler, Mohamed Alhaskir, Mirabela Rusu, Christiane Kuhl, Sven Nebelung, Daniel Truhn
arXiv:2609.13271v1 Announce Type: cross
Abstract: Medical image segmentation needs diverse training data, but hospitals hold complementary scans they cannot share for privacy and regulatory reasons....
By Armaghan Butt, Shuya Feng, Qing Tian
arXiv:2601. 14033v2 Announce Type: replace Abstract: Machine learning models are increasingly served behind APIs.
By Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas
arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.
By Rasmus Torp, Shailen K. Smith, Adam Breuer
arXiv:2609.26623v1 Announce Type: new
Abstract: Diffusion-based synthetic data generation offers a promising route for sharing medical imaging data without releasing sensitive patient records. Howeve...
By Mischa Dombrowski, Bernhard Kainz
arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.
By Fardin Jalil Piran, Zhiling Chen, Yang Zhang, Qianyu Zhou, Jiong Tang, Farhad Imani
Aegis is a client‑side defense for medical federated learning that protects against model inversion attacks by adding a masking gradient derived from locally synthesized data. The method exploits the fact that attacks fail when the effective batch size exceeds the model’s leakage capacity, turning this bottleneck into a privacy guarantee. Experiments on MNIST, CIFAR‑10, and MedMNIST datasets show that Aegis neutralizes state‑of‑the‑art attacks while preserving model accuracy and adding only modest overhead.
By Chaoyu Zhang, Shanghao Shi, Heng Jin, Ning Wang, Y. Thomas Hou, Wenjing Lou
The paper compares four audit methods for assessing identity‑level differential privacy in pre‑trained, black‑box face generators. Each method—GaussMech, KDE‑LR, MMD‑TV, and ROC‑HT—has distinct assumptions, hyperparameters, and finite‑sample limitations, and they produce markedly different epsilon estimates when applied to FaceFusion and InstantID. The study finds that all methods reveal significant identity distinguishability, but none can be reliably ranked in this high‑distinguishability regime, suggesting that future work should evaluate them on partially private mechanisms.
By Arman Zareian Jahromi, Vishnu Bondalakunta, Mohammad Akbar Bin Shah, Naimul Haque, Shuangqing Wei, George T. Amariucai