Federated Learning in Medical Diagnostics

Abstract

AI has demonstrated its necessity in healthcare through better disease forecasts. Conventional machine learning models function on data that are gathered in a centralized location, which require data exchange and thus cause worries about privacy of patients, data safety, and obeying rules. Federated Learning (FL) brings paradigm shift by allowing multiple institutions to train their AI models collectively, without transferring any raw patient data. In this chapter, we investigate how FL can change the face of medical diagnostics. Recalling insights from existing machine learning models for Chronic Kidney Disease detection. We discuss how FL can avoid sharing the actual data sets among the multiple parties and retain predictive accuracy and interpretability of models. We also cover Explainable AI approaches such as SHAP and LIME which serve to increase transparency of FL-based diagnostics. In presenting a full framework for ethical and privacy-preserving AI for healthcare, this chapter brings to the foreground the huge potential of FL for next generation of Medical Intelligence.

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