Federated Learning in Healthcare
Muhammad Hamza · 2024
Machine learning (ML) has become a promising approach for building robust and accurate models that are driven by data and these models are developed and trained on medical records. Due to their sensitive nature and privacy issues healthcare records collaboration, security, and privacy have been a very difficult task to deal with. Federate learning (FL) has emerged as a collaborative, privacy-preserving, and secure technique. We get almost the same or even better results when compared to the classical ML models and the benefits of FL outnumber those we have from classical ML. FL provides better privacy-preserving and anonymity while providing us with better predictions, precision, and accuracy for any given task. Models that are trained using FL can be utilized to analyze medical records, sensory data, and medical images and provide better decision-making. This can only be achieved if we are to implement FL on a global scale while overcoming the challenges and issues that we face in FL. We will be exploring the impacts it can have on healthcare and how it preserves privacy while being trained on highly sensitive medical data and provide predictions that can actually have positive effects on a global scale and how it can bring equality and fairness in healthcare so everyone gets the same quality of treatment and we will discuss the future directions that are necessary to reshape our healthcare to a much better FL based digital healthcare.