Open Access

ARTICLE

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

by Jinbo Yang¹, Hai Huang¹, Lailai Yin², Jiaxing Qu³, Wanjuan Xie^4,*

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

* Corresponding Author: Wanjuan Xie. Email:

(This article belongs to the Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)

Computer Modeling in Engineering & Sciences 2024, 139(3), 3085-3099. https://doi.org/10.32604/cmes.2023.045417

Received 26 August 2023; Accepted 14 November 2023; Issue published 11 March 2024

Abstract

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.

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Keywords

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