Federated Multi-Task Learning to Solve Various Healthcare Challenges
Seema Pahwa, Amandeep Kaur · 2024
In this study, Federated Multi-Task Learning (FMTL) is investigated as a viable remedy for challenging healthcare problems. Decentralized machine learning is used by FMTL to collect information from various sources while maintaining security and privacy. It is used for many healthcare tasks, including disease prediction, individualized treatment, resource management, and patient outcome analysis. For successful multi-task learning across several healthcare domains, a unique federated learning system is developed. The study shows how FMTL can increase prediction accuracy while lowering expenses and enhancing patient outcomes. The importance of responsible data usage and privacy protection is emphasized while the moral and legal ramifications are also explored. The results offer a road map for adopting FMTL in real-world hospital settings, ultimately enhancing patient care and healthcare services.