论文标题
通过匿名随机杂交（fearh）在医疗记录上使用联合机器学习
Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
论文作者
论文摘要
有时，电气病历受到限制，很难集中用于机器学习，只能以分布式方式进行培训，以涉及许多机构。但是，有时某些机构可能会根据所获得的参数来弄清用于培训某些模型的私人数据，这违反了隐私和某些法规。在这种情况下，我们开发了一种算法，称为“使用匿名随机杂交的联合机器学习”（缩写为“ fearh”），主要使用杂交算法来消除医疗记录数据和模型参数之间的联系，从而避免了从窃取不信任的机构从窃取患者的不信任机构。基于我们的实验，与以集中式的方式和原始的联合机器学习相比，我们的新算法具有相似的AUCROC和AUCPR结果，与此同时，与原始联合机器学习相比，我们的算法可以大大降低数据传输尺寸。
Sometimes electrical medical records are restricted and difficult to centralize for machine learning, which could only be trained in distributed manner that involved many institutions in the process. However, sometimes some institutions are likely to figure out the private data used for training certain models based on the parameters they obtained, which is a violation of privacy and certain regulations. Under those circumstances, we develop an algorithm, called 'federated machine learning with anonymous random hybridization'(abbreviated as 'FeARH'), using mainly hybridization algorithm to eliminate connections between medical record data and models' parameters, which avoid untrustworthy institutions from stealing patients' private medical records. Based on our experiment, our new algorithm has similar AUCROC and AUCPR result compared with machine learning in centralized manner and original federated machine learning, at the same time, our algorithm can greatly reduce data transfer size in comparison with original federated machine learning.
