针对医疗影像联邦学习中的推理攻击，提出三阶段防护框架，提升诊断模型安全性。
MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks
- 采用个性化联邦学习+自编码器检测异常输入
- 对抗攻击下准确率从49.50%提升至87.33%，干净图像准确率达97.67%
- 本地实时运行，适合临床部署，兼顾隐私与诊断可信度
人工智能在医学影像领域潜力巨大，尤其在基于磁共振成像（MRI）的脑肿瘤检测中表现突出。然而，在通过联邦学习（FL）协作训练时，模型在推理阶段仍易受对抗攻击影响，攻击者可对影像进行人眼不可见的微小扰动，导致严重误诊。现有防御方法多依赖集中式数据，难以适应去中心化、多样化的联邦医疗环境。本文提出MedFedPure，一种面向推理时攻击的个性化联邦学习防御框架。该框架融合三项核心技术：（1）个性化联邦模型，适配各机构独特数据分布；（2）基于掩码自编码器（MAE）的可疑输入检测机制，可揭示隐藏扰动；（3）自适应扩散净化模块，仅对标记异常的扫描进行局部清洗后分类。实验基于Br35H脑部MRI数据集验证，结果显示，在强攻击下模型性能从49.50%提升至87.33%，同时保持97.67%的干净准确率。该框架可在本地实时运行，为临床工作流提供安全、可信、隐私保护的AI工具路径。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has shown great potential in medical imaging, particularly for brain tumor detection using Magnetic Resonance Imaging (MRI). However, the models remain vulnerable at inference time when they are trained collaboratively through Federated Learning (FL), an approach adopted to protect patient privacy. Adversarial attacks can subtly alter medical scans in ways invisible to the human eye yet powerful enough to mislead AI models, potentially causing serious misdiagnoses. Existing defenses often assume centralized data and struggle to cope with the decentralized and diverse nature of federated medical settings. In this work, we present MedFedPure, a personalized federated learning defense framework designed to protect diagnostic AI models at inference time without compromising privacy or accuracy. MedFedPure combines three key elements: (1) a personalized FL model that adapts to the unique data distribution of each institution; (2) a Masked Autoencoder (MAE) that detects suspicious inputs by exposing hidden perturbations; and (3) an adaptive diffusion-based purification module that selectively cleans only the flagged scans before classification. Together, these steps offer robust protection while preserving the integrity of normal, benign images. We evaluated MedFedPure on the Br35H brain MRI dataset. The results show a significant gain in adversarial robustness, improving performance from 49.50% to 87.33% under strong attacks, while maintaining a high clean accuracy of 97.67%. By operating locally and in real time during diagnosis, our framework provides a practical path to deploying secure, trustworthy, and privacy-preserving AI tools in clinical workflows. Index Terms: cancer, tumor detection, federated learning, masked autoencoder, diffusion, privacy
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。