Survey: An Overview on Privacy Preserving Federated Learning in Health Data

Abstract

Machine learning now confronts two significant obstacles: the first is data isolation in most organizations' silos, and the second is data privacy and security enforcement. The widespread application of Machine Learning techniques in patient care is currently hampered by limited dataset availability for algorithm training and validation due to the lack of standardised electronic medical records and strict legal and ethical requirements to protect patient privacy. To avoid compromising patient privacy while supporting scientific analysis on massive datasets to improve patient care, it is necessary to analyse and implement Machine Learning solutions that fulfil data security and consumption demands. In this survey paper, we meticulously explain the existing works of federated learning from many perspectives to give a thorough overview and promote future research in this area. Then, we determine the current challenges, attack vectors and potential prospects for federated learning research. We analysed the similarities, differences and advantages between federated learning and other machine learning techniques. We also discussed about system and statistical heterogeneity and related efficient algorithms.