Newest papers

Privacy-Preserving Personalized Federated Learning (PPP-FL) for Medical Imaging

Md Morshedul Islam, Hossain Shahriar, Alfredo Cuzzocrea · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27470 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

联邦范围学习(FL)是一种分布式机器学习范式,可降低传输成本,减轻跨境数据共享限制,并通过在本地设备上保留原始数据来增强隐私。FL系统中的客户只训练本地模型,并共享更新,这些数据由中央服务器汇集。虽然这个设置限制了直接数据暴露,但FL仍然容易受到隐私攻击。现有的攻击和防御策略在很大程度上是针对中心化系统开发的,并且将其适应到FL会引入新的挑战,因为其独特的架构和培训工作流程。在这项工作中,我们首先提出了对FL系统的模型逆转攻击,使用深波动性生成对手网络(DCGAN)在大规模医疗成像背景下受到影响。我们的方法在对攻击者知识和能力的现实假设下

Machine translation (Meta M2M100); the English original follows.

Abstract

Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions, and enhances privacy by keeping raw data on local devices. Clients in FL systems train local models and share only updates, which are aggregated by a central server. While this setup limits direct data exposure, FL remains vulnerable to privacy attacks. Existing attack and defense strategies have largely been developed for centralized systems, and adapting them to FL introduces new challenges due to its unique architecture and training workflow. In this work, we first propose a model inversion attack on FL systems using Deep Convolutional Generative Adversarial Networks (DCGANs) in the context of large-scale medical imaging. Our method operates under realistic assumptions about the attacker's knowledge and capabilities. We then examine the effect of client-side differential privacy (DP) in mitigating privacy leakage and incorporate personalized FL to improve model performance under DP settings. Experiments on the Brain MRI ND-5 dataset show that DCGAN-based attacks can reconstruct semantically meaningful synthetic images, revealing significant risks to the privacy of training data. Applying DP reduces attack success but introduces an accuracy trade-off, which is further overcome by personalized FL at each client side. We also evaluate how different aggregation algorithms affect both attack vulnerability and defense effectiveness, underscoring the critical role of aggregation strategies. The proposed attack-and-defense framework is model-agnostic and applicable to a wide range of FL-based systems.

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Topics: Computer science, Federated learning, Differential privacy, Adversarial system, Context (archaeology), Raw data

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