Early Detection of Diabetes Mellitus Using Differentially Private SGD in Federated Learning

Abstract

Diabetes mellitus is a chronic disease that appears when the pancreas does not produce enough insulin or the body does not correctly use its insulin. If not adequately managed or diagnosed on time, this pathology can cause a lot of damage to the body organs, such as the heart, eyes, kidneys, and so on. Research carried out through machine learning has made it possible to have increasingly efficient and precise models for detecting and preventing type 2 diabetes. However, most of the models mentioned do not offer guarantees on the privacy of patient data used during the training process. In addition, these models are generally stored in a centralized repository, where the analysis is performed with full access to sensitive content, implying increased attack risks on confidentiality and privacy. This paper proposes a Differentially Private Stochastic Gradient Descent applied to the Federated Averaging (DPSGDFedAvg) model for diabetes prediction using the Pima Indian dataset. In first results, we obtained an accuracy between 60% and 70% with a raised level of privacy. We demonstrate in this work the feasibility and effectiveness of the DPSGDFedAvg model in offering a raised level of privacy and maintaining utility of the global FL model.

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