Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
This addresses privacy concerns for medical institutions handling sensitive patient data, though it appears incremental as it builds on existing federated learning methods.
The paper tackles the problem of privacy violations in federated learning on medical records by developing the FeARH algorithm, which uses hybridization to sever connections between data and model parameters, achieving similar AUCROC and AUCPR to centralized and original federated learning while greatly reducing data transfer size.
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.