EHR privacy preservation using federated learning with DQRE-Scnet for healthcare application domains

Abstract

A distributed learning technique named Federated Learning (FL) is utilized by mobile devices, clinical research labs, and hospitals for secure healthcare data sharing. FL has been utilized by several researchers for preserving the privacy of Electronic Health Record (EHR) information while updating the global server. Still, the privacy and higher number of communication rounds limit the performance of the model. Hence, this research introduces the privacy-preserving EHR information sharing technique, FL, based on deep Q reinforcement learning with spectral clustering (FL+DQRE-SCnet). In this, the local healthcare information is trained using the DCNN in the respective hospitals based on FL criteria. After learning the local model, the information is shared with the global model for updating the EHR to enhance the accuracy of disease diagnosis. The global model aggregates the information from the local models over several communication rounds by randomly selecting the local models, increasing the need for more communication rounds. Therefore, DQRE-SCnet is introduced before data aggregation to minimize the communication rounds. In addition, including a homomorphic encryption technique ensures privacy protection for FL data sharing. The analysis of FL+DQRE-SCnet based on the assessment measures of accuracy, precision, recall, and F-measure acquired maximal values of 95%, 94.9%, 94.94%, and 93.94%, respectively. Also, the error rate of 0.46 and the execution time of 1400 sec are accomplished by the FL+DQRE-SCnet method. Here, for comparing the performance of the model, the baseline methods like FedAvg, LoAdaBoost, BOFRF, PP-FedAvg and AdaFed are considered, wherein the proposed method acquired superior performance.