Adaptive Federated Deep Learning With Non-IID Data

被引：0
作者：
Zhang, Ze-Hui
Li, Qing-Dan
Fu, Yao
He, Ning-Xin
Gao, Tie-Gang
机构：
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 12期
关键词：
chaos system; deep learning; Federated learning (FL); homomorphic encryption; privacy-preserving;
D O I：
10.16383/j.aas.c201018
中图分类号：
学科分类号：
摘要：
In recent years, federated learning (FL) that can break data barriers and realize the value of isolated data, has been received wide-spread attention from industry and academia. However, in real industry applications, federated learning has problems with privacy leakage and model accuracy loss, which is analyzed through mathematical demonstration in this study. To solve the issues, this paper proposes an adaptive global model aggregation scheme that can adaptively set the Mini-batch value of each participant and the global model aggregation interval for the parameter server, which aims to improve the training efficiency while ensuring the accuracy of the model. Moreover, this paper introduces the chaos system into the federated learning field, which is used to construct a hybrid privacy-preserving scheme based on chaos system and homomorphic encryption, thereby further improving the privacy protection level. Theoretical analysis and experimental results show that the proposed approach can guarantee the data privacy security of participants. Moreover, in the non-independent and identically distributed (Non-IID) data scenario, the proposed method can improve the training efficiency and reduce communication cost while ensuring the model accuracy, which is feasible for real industrial applications. © 2023 Science Press. All rights reserved.
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页码：2493 / 2506
页数：13
相关论文
共 36 条
  • [21] Wang Y F, Gu M, Ma J H, Jin Q., DNN-DP: Differential privacy enabled deep neural network learning framework for sensitive crowdsourcing data, IEEE Transactions on Computational Social Systems, 7, 1, pp. 215-224, (2020)
  • [22] Carpov S, Gama N, Georgieva M, Troncoso-Pastoriza J R., Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption, BMC Medical Genomics, 13, 7, (2019)
  • [23] Song Lei, Ma Chun-Guang, Duan Guang-Han, Yuan Qi, Privacy-preserving logistic regression on vertically partitioned data, Journal of Computer Research and Development, 56, 10, pp. 2243-2249, (2019)
  • [24] Phong L T, Aono Y, Hayashi T, Wang L H, Moriai S., Privacy-preserving deep learning via additively homomorphic encryption, IEEE Transactions on Information Forensics and Security, 13, 5, pp. 1333-1345, (2018)
  • [25] Ou W, Zeng J H, Guo Z J, Yan W Q, Liu D W, Fuentes S., A homomorphic-encryption-based vertical federated learning scheme for rick management, Computer Science and Information Systems, 17, 3, pp. 819-834, (2020)
  • [26] Chen H, Chillotti I, Song Y., Improved bootstrapping for approximate homomorphic encryption, Proceedings of the 38th Annual International Conference on the Theory and Applications of Cryptographic Techniques, pp. 34-54, (2019)
  • [27] Xiao X D, Wu T, Chen Y F, Fan X Y., Privacy-preserved approximate classification based on homomorphic encryption, Mathematical and Computational Applications, 24, 4, (2019)
  • [28] Zhang Z H, Yao F, Gao T G., A hybrid image encryption algorithm based on chaos system and simplified advanced encryption system, International Journal of Multimedia Data Engineering and Management (IJMDEM), 11, 4, (2020)
  • [29] Luo Y Q, Yu J, Lai W R, Liu L F., A novel chaotic image encryption algorithm based on improved baker map and logistic map, Multimedia Tools and Applications, 78, 15, pp. 22023-22043, (2019)
  • [30] Sathiyamurthi P, Ramakrishnan S., Speech encryption algorithm using FFT and 3D-Lorenz-logistic chaotic map, Multimedia Tools and Applications, 79, 25, pp. 17817-17835, (2020)
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