A Systematic Review on Federated Learning in Medical Image Analysis

Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from beginning its invention. The eye-catching feature FL is handling data in decentralized manner which creates privacy preserving environment Artificial Intelligence (AI) applications. As we know medical includes marginal private information patients demands excessive protection disclosure unexpected destinations. In this paper, performed Systematic Literature Review (SLR) published research articles on based image analysis. Firstly, have collected different databases followed by PRISMA guidelines, then synthesized selected articles, finally provided comprehensive overview topic. order do that extracted core associated with implementation imaging articles. our findings briefly presented characteristics federated models, performance achieved models exclusively results comparison traditional ML models. addition, discussed open issues challenges implementing mentioned recommendations for future direction particular field. We believe SLR has successfully summarized state-of-the-art methods analysis using deep learning.