Revolutionizing Medical Image Segmentation: A Deep Dive into Challenges and Future of Federated Learning

The possibility of medical image segmentation within the domain of a federated learning, Federated Learning (FL) may transform the situation and help solve the critical challenges that exist in common centralized machine learning models. While effective, traditional models are limited by issues like the need of huge surveys, high costs in data assignment, high privacy concerns over sensible wellbeing data. Since improvements in the medical imaging field continue, the adoption of FL is a strategic response to such limitations and can be introduced as a collaborative privacy preserving framework for model training. This was a systematic exploration of the literature from 2017 to 2024 where the Google Scholar literature has been explored for studies indexed with the keywords 'federated learning,' 'medical image segmentation,' and 'privacy preservation.' Specifically, this review did not consider studies that did not directly discuss FL concepts. Twenty-one publications were carefully selected from out of thousands of publications because they are relevant and contribute to the area of treatment. Specifically, seven studies directly approached the extent of medical image segmentation using FL and address the technological and the practical challenges. The remaining fourteen studies were foundational in that they further elaborated on the architectural and procedural elements of FL frameworks that are essential for collaborative and secure medical image analysis. A review of the selected studies is presented in detail in the review in terms of the effectiveness of FL in improving medical image segmentation while protecting patient privacy. It makes a powerful evaluation of the strengths and weakness of present FL model, the versatility of data sets, the diversity of the imaging modalities addressed, and scalability of these models across various clinical conditions. Such synthesis of this literature underscores the fact that FL can revolutionize medical diagnostics with opportunity to produce more robust, scalable, and privacy friendly models.