Model split-based data privacy protection method for federated learning

CHEN Ka

doi:10.11959/j.issn.1000-0801.2024206

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Model split-based data privacy protection method for federated learning

Research and Development | 更新时间：2024-11-25

- Model split-based data privacy protection method for federated learning
- Telecommunications Science Vol. 40, Issue 9, Pages: 136-145(2024)
- 作者机构：
  
  驻马店职业技术学院，河南驻马店 463000
- 作者简介：
- 基金信息：
  
  Science and Technology Development Plan of Henan Province in 2024(242400410491);Science and Technology Research Plan of Henan Province(212102210516);Henan Province Higher Education Teaching Reform Research and Practice Project in 2021(2021SJGLX865)
- DOI：10.11959/j.issn.1000-0801.2024206
  CLC： TN18
- Received：02 March 2024，
  
  Revised：2024-09-05，
  
  Published：20 September 2024
- 稿件说明：
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陈卡.基于模型分割的联邦学习数据隐私保护方法[J].电信科学,2024,40(09):136-145.

CHEN Ka.Model split-based data privacy protection method for federated learning[J].Telecommunications Science,2024,40(09):136-145.
陈卡.基于模型分割的联邦学习数据隐私保护方法[J].电信科学,2024,40(09):136-145. DOI： 10.11959/j.issn.1000-0801.2024206.

CHEN Ka.Model split-based data privacy protection method for federated learning[J].Telecommunications Science,2024,40(09):136-145. DOI： 10.11959/j.issn.1000-0801.2024206.

摘要

在不共享原始数据的前提下，分割学习（split learning，SL）允许客户端同服务端协作训练深度学习模型，进而保护数据隐私。然而，SL仍存在数据隐私泄露问题。为此，提出基于二值分割学习的数据隐私保护（binarized split learning-based data privacy protection，BLDP）算法。将客户端所训练的本地模型进行二值化，降低由分割层输出值引起的数据泄露损失。同时，BLDP算法采用泄露约束训练机制，进一步减少数据泄露损失。该机制以本地数据泄露损失和模型精度损失为总体损失值进行模型训练，进而在维护模型精度的同时，保护数据隐私。以4个常用的基准数据集进行训练，分析BLDP算法的分类准确率以及减少数据隐私泄露损失方面的性能。分析结果表明，所提BLDP算法能在分类准确率和数据隐私泄露损失间达成平衡。

Abstract

Split learning (SL) enables data privacy preservation by allowing clients to collaboratively train a deep learning model with the server without sharing raw data. However

the SL still has limitations such as potential data privacy leakage. Therefore

binarized split learning-based data privacy protection (BLDP) algorithm was proposed. In BLDP

the local layers of client were binarized to reduce privacy leakage from SL smashed data. In addition

the leakage-restriction training strategy was proposed to further reduce data leaks. The strategy combines leak loss of local private data and model accuracy loss that enhances privacy while maintaining model accuracy. To evaluate the proposed BLDP algorithm

experiments were conducted on four commonly benchmarked datasets and the leakage loss and model accuracy were analyzed. The results show that the proposed BLDP algorithm can achieve a balance between classification accuracy and data privacy loss.

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references

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