DeFedHDP: Fully Decentralized Online Federated Learning for Heart Disease Prediction in Computational Health Systems

Abstract

Heart disease is a leading global cause of death, while federated learning (FL) is an effective way to predict it. Due to patient privacy concerns and the centralized nature of current FL approaches, collaborative research in heart disease prediction (HDP) faces significant hurdles in computational health systems. This article introduces a distributed online aggregation method within fully decentralized federated learning (DFL), named DeFedHDP, to address these privacy challenges and improve the model of HDP. Moreover, the differential privacy (DP) mechanism is applied to the aggregation strategy of DeFedHDP to protect the privacy of the patients. The data holder communicates directly with neighbors in a series of time-varying directed graphs without the involvement of a central server. Furthermore, each participant is both a trainer of the local model and a collaborator of the other participants’ models. The data does not leave the local device, only the model parameters are exchanged and integrated, and this decentralized approach can further improve the level of privacy protection. In addition, to cope with model gradient disappearance and gradient explosion, the one-point bandit feedback (OPBF) strategy is utilized to estimate the true gradient values. Experiments on a public medical dataset show that the effectiveness of DeFedHDP is close to the centralized FedAVG algorithm for client-server architectures in terms of accuracy and speed.