首页|Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution
Adaptive 5G-and-beyond network-enabled interpretable federated learning enhanced by neuroevolution
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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.
5G-and-beyond networkinterpretable federated learningmobile telemedicine systemfuzzy rough set theoryneuroevolution
Bin CAO、Jianwei ZHAO、Xin LIU、Yun LI
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State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300401,China
School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China
School of Economics and Management,Hebei University of Technology,Tianjin 300401,China
Industrial Artificial Intelligence Centre,Shenzhen Institute for Advanced Study,University of Electronic Science and Technology of China,Shenzhen 518110,China
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Natural Science Fund of Hebei Province for Distinguished Young ScholarsScience and Technology Project of Hebei Education DepartmentS&T Program of Hebei
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