保障患者数据共享:医疗诊断中的区块链联合学习

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Raushan Myrzashova;Saeed Hamood Alsamhi;Ammar Hawbani;Edward Curry;Mohsen Guizani;Xi Wei
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引用次数: 0

摘要

医疗保健中心被设想为一个有前途的范例,使用人工智能处理各种疾病诊断的大量数据。传统的机器学习算法已经使用了多年,这使得患者医疗数据隐私的敏感性面临风险。通过多家医院(节点)训练并共享加密联邦模型的协同数据训练,解决了数据泄露问题,实现了异地大小医院资源的统一。本研究引入了一个创新框架,利用基于区块链的联邦学习来识别15种不同的肺部疾病,确保保护隐私和安全。该模型已在NIH Chest Ray数据集(112,120张x射线图像)上进行了训练,并进行了测试和评估,测试准确率为92.86%,延迟为43.518625 ms,吞吐量为10,034,017字节/秒。此外,我们将我们的框架区块链暴露于针对主要网络威胁的严格实证测试中,以评估其稳健性。针对三种已评估的网络攻击,该框架的弹性指标始终接近87%,表明该框架在医疗保健应用中具有显著的稳健性和潜力。据我们所知,这是第一篇关于使用这些数据和几种疾病(包括多种疾病共存检测)实际实施区块链授权FL的论文。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Safeguarding Patient Data-Sharing: Blockchain-Enabled Federated Learning in Medical Diagnostics
Medical healthcare centers are envisioned as a promising paradigm to handle vast data for various disease diagnoses using artificial intelligence. Traditional Machine Learning algorithms have been used for years, putting the sensitivity of patients’ medical data privacy at risk. Collaborative data training, where multiple hospitals (nodes) train and share encrypted federated models, solves the issue of data leakage and unites resources of small and large hospitals from distant areas. This study introduces an innovative framework that leverages blockchain-based Federated Learning to identify 15 distinct lung diseases, ensuring the preservation of privacy and security. The proposed model has been trained on the NIH Chest Ray dataset (112,120 X-Ray images), tested, and evaluated, achieving test accuracy of 92.86%, a latency of 43.518625 ms, and a throughput of 10,034,017 bytes/s. Furthermore, we expose our framework blockchain to stringent empirical tests against leading cyber threats to evaluate its robustness. With resilience metrics consistently nearing 87% against three evaluated cyberattacks, the proposed framework demonstrates significant robustness and potential for healthcare applications. To the best of our knowledge, this is the first paper on the practical implementation of blockchain-empowered FL with such data and several diseases, including multiple disease coexistence detection.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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