The Future of Digital Health with Federated Learning
This work tackles data access issues in healthcare for improving ML applications, but it is incremental as it reviews existing concepts without presenting new results.
The paper addresses the problem of underutilized medical data due to privacy concerns and data silos, which hinders machine learning from transitioning to clinical practice, by exploring how Federated Learning can provide a solution and highlighting associated challenges.
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how Federated Learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.