PrivFED——一种用于增强型乳腺癌诊断的隐私保护联邦学习框架
PrivFED -- A Framework for Privacy-Preserving Federated Learning in Enhanced Breast Cancer Diagnosis
AI总结：
针对医疗数据交换的隐私安全及数据稀缺、不平衡问题，本研究提出隐私保护联邦学习框架PrivFED，结合SMOTE、孤立森林、Catboost与PCA，在Wisconsin数据集上实现边缘设备99.95%、中心服务器98%的乳腺癌诊断准确率。
摘要：
在医疗机构的日常运营中，会产生大量个人身份信息（PII）数据交换，使数据面临各类网络安全威胁。本研究提出一种联邦学习框架，在Wisconsin数据集上训练，以缓解数据稀缺和不平衡等挑战。该框架整合了合成少数类过采样技术（SMOTE）以提升鲁棒性，同时采用孤立森林增强模型对异常值的抵御能力，所有设备均使用Catboost作为分类工具。研究通过主成分分析（PCA）寻找可提升准确率的最优特征，对比分析也凸显了超参数调优的重要性。该模型在边缘设备上的平均准确率为99.95%，在中央服务器上为98%。
英文摘要：
In the day-to-day operations of healthcare institutions, a multitude of Personally Identifiable Information (PII) data exchanges occur, exposing the data to a spectrum of cybersecurity threats. This study introduces a federated learning framework, trained on the Wisconsin dataset, to mitigate challenges such as data scarcity and imbalance. Techniques like the Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to bolster robustness, while isolation forests are employed to fortify the model against outliers. Catboost serves as the classification tool across all devices. The identification of optimal features for heightened accuracy is pursued through Principal Component Analysis (PCA),accentuating the significance of hyperparameter tuning, as underscored in a comparative analysis. The model exhibits an average accuracy of 99.95% on edge devices and 98% on the central server.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
