Secure Future Healthcare Applications Through Federated Learning Approaches


Tabassum M., Kuzlu M., Catak F. O., Sarp S., ŞAHİNBAŞ K.

2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA 2023, İstanbul, Turkey, 10 - 11 March 2023, vol.1983 CCIS, pp.214-225 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1983 CCIS
  • Doi Number: 10.1007/978-3-031-50920-9_17
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.214-225
  • Keywords: Artificial Intelligence, Deep Learning, Federated Learning, Healthcare, Machine Learning, Privacy
  • Istanbul Medipol University Affiliated: Yes

Abstract

The healthcare field is so sensitive to data privacy and security due to including medical and personal information. Almost all healthcare applications are required to increase data security and privacy, which use traditional machine learning approaches relying on centralized systems, both computing resources and the entirety of the data. Federated learning, a sort of machine learning technique, has been used to exactly address this issue. The training data is disseminated across numerous devices in federated learning, and the learning process is collaborative. There are numerous privacy attacks on Deep Learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in healthcare applications that use sensitive medical data. This paper provides a comprehensive review of federated learning on future healthcare applications. It also discusses the types of federated learning along with its implementation in healthcare applications.