Risk and Advantages of Federated Learning for Health Care Data Collaboration

Abstract

This paper explores the problem of data collaboration in health care, which is the one of the critical infrastructure sectors designated by the Department of Home Security. Limitations to data sharing in health care obstruct the development of a new generation of medical technology powered by artificial intelligence (AI). Collaborative machine learning helps to overcome these limitations through training models on distributed data sets without data sharing. Among other approaches to collaborative machine learning, federated learning in recent years has demonstrated multiple advantages. However, it had been developed and tested in a highly distributed data environment, which is different from the typical cases of health care data collaboration. The objective of this paper is to validate the known advantages of federated learning and to assess possible risks in a small multiparty setting. The experiments show that federated learning can be successfully applied in a multiparty collaboration setting. However, with a small number of parties, it becomes easier to overfit to each local data so that the averaging steps have to occur more frequently. In addition, for the first time, the risks of a membership inference attack were assessed for different methods of collaborative machine learning.