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Emerging Trends in Personalized Medicines Review Article

A Comprehensive Review on Federated Learning in Disease Detection

  1. Projesh Saha Jakir Hossain Institute of Pharmacy
  2. Upasna Rai Department of Pharmaceutical Analysis, Kaziranga University
  3. Disha Bhattacharjee Ecole Polytechnique
  4. Poulami Chhetri Department of Agricultural Economics, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar

Abstract

Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights while maintaining individual privacy. The use of federated learning to improve disease diagnosis and predictive health analytics' accuracy is demonstrated in this article. It investigates how, even in situations where data is localised at its source, Federated Learning architectures enable the creation of potent diagnostic and prognostic models. Federated Learning reduces the danger of data breaches and guarantees adherence to strict privacy laws such as the Health Insurance Portability and Accountability Act and the General Data Protection Regulation by encouraging collaborative learning and limiting data exposure. By overcoming the constraints imposed by the availability of data at a single site, this collaborative approach encourages the development of more accurate and generalised models. This article looks at a number of Federated Learning approaches that are relevant to the healthcare industry, including methods for managing system imbalances and data heterogeneity among participating nodes. It goes over real-world examples that show the way Federated Learning may be used to find illness biomarkers, forecast patient outcomes, and improve treatment regimens. It also discusses contemporary issues including model personalisation, communication overhead, and security flaws unique to healthcare federated learning. The article concludes by outlining potential research avenues and future directions, highlighting Federated Learning's critical role in influencing the growth of the next wave of privacy-preserving, Artificial Intelligence- driven healthcare solutions.

Keywords

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