Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Abstract

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.

ACM Transactions on Computing for Healthcare, Vol. 3, No. 4, Article 40. Publication date: October 2022

References in corpus (9)

• Poisoning Attacks against Support Vector Machines
• Threats to Federated Learning: A Survey
• Towards the Science of Security and Privacy in Machine Learning
• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
• Federated Evaluation of On-device Personalization
• Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
• MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
• Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
• Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach