Abstract
Mobile telemedicine systems based on the next-generation communication will significantly en-hance deep fusion of network automation and federated learning(FL),but data privacy is a paramount issue in sectors like healthcare.This work hence considers FL augments 5G-and-beyond networks by training deep learning(DL)models without the need to exchange raw data.The substantial communication loads imposed on by extensive parameters involved in DL models are managed through adaptive scheduling mechanisms effectively.To address the opaque nature of DL models and to improve the interpretability of FL models,we introduce a convolutional fuzzy rough neural network specifically designed for medical image processing.We also develop a multiobjective memetic evolutionary algorithm to streamline and optimize the neural network architectures.Our comprehensive FL framework integrates smart scheduling,interpretable fuzzy rough logic,and neuroevolution.This framework is shown to improve communication efficiency,increase interpretability of diagnosis with protected privacy,and generate low-complexity neural architectures.
基金项目
Natural Science Fund of Hebei Province for Distinguished Young Scholars(F2021202010)
Science and Technology Project of Hebei Education Department(JZX2023007)
S&T Program of Hebei(225676163GH)
NETL
NSTL
万方数据