Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases  

作　　者：Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie 

机构地区：[1]School of Computer Science and Technology,Harbin University of Science and Technology,Harbin,150080,China [2]Shanghai Futures Information Technology Co.,Ltd.,Shanghai,201201,China [3]Heilongjiang Province Cyberspace Research Center,Harbin,150001,China [4]Information Network Engineering and Research Center,South China University of Technology,Guangzhou,510641,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期3085-3099,共15页工程与科学中的计算机建模（英文）

基　　金：funded by the National Natural Science Foundation,China(No.62172123);the Key Research and Development Program of Heilongjiang(Grant No.2022ZX01A36);the Special Projects for the Central Government to Guide the Development of Local Science and Technology,China(No.ZY20B11);the Harbin Manufacturing Technology Innovation Talent Project(No.CXRC20221104236).

摘　　要：Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic data.When applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be addressed.Firstly,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured data.Secondly,privacy of patients’data needs to be protected,as these data contain the most sensitive and private information.Lastly,considering the practicality of the model,the computational requirements should not be too high.To address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases.This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter.It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in modeling.Finally,this paper designs and conducts experiments to evaluate the proposed solution.The experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关 键 词：Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data 

分 类 号：R44[医药卫生—诊断学] TP18[医药卫生—临床医学]