Research on asynchronous robust federated learning method in vehicle computing power network

YIN Hongbo; WANG Shuai; ZHANG Ke; ZHANG Yin

doi:10.11959/j.issn.2096-3750.2024.00452

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Research on asynchronous robust federated learning method in vehicle computing power network

Topic: Computing Power Internet of Things | 更新时间：2025-01-20

- Research on asynchronous robust federated learning method in vehicle computing power network
- Chinese Journal on Internet of Things Vol. 8, Issue 4, Pages: 14-22(2024)
- 作者机构：
  
  1.电子科技大学信息与通信工程学院，四川成都 611731
  2.电子科技大学（深圳）高等研究院，广东深圳 518110
  3.广东省智能机器人研究院，广东东莞 523830
- 作者简介：
- 基金信息：
  
  The Key Research and Development Program of Guangdong Province(2024B1111060001)
- DOI：10.11959/j.issn.2096-3750.2024.00452
  CLC：
- Received：18 November 2024，
  
  Revised：2024-12-10，
  
  Published：10 December 2024
- 稿件说明：
移动端阅览
尹宏博,王帅,张科等.车辆算力网络中异步鲁棒联邦学习方法研究[J].物联网学报,2024,08(04):14-22.

YIN Hongbo,WANG Shuai,ZHANG Ke,et al.Research on asynchronous robust federated learning method in vehicle computing power network[J].Chinese Journal on Internet of Things,2024,08(04):14-22.
尹宏博,王帅,张科等.车辆算力网络中异步鲁棒联邦学习方法研究[J].物联网学报,2024,08(04):14-22. DOI： 10.11959/j.issn.2096-3750.2024.00452.

YIN Hongbo,WANG Shuai,ZHANG Ke,et al.Research on asynchronous robust federated learning method in vehicle computing power network[J].Chinese Journal on Internet of Things,2024,08(04):14-22. DOI： 10.11959/j.issn.2096-3750.2024.00452.

摘要

传统联邦学习的同步训练机制并不适用于动态的车辆算力网络场景，且在恶意车辆攻击的威胁下，缺乏有效的攻击检测机制。为了解决以上问题，首先，提出一种异步鲁棒联邦学习方法，通过车辆之间异步地执行联邦学习过程，在实现车辆数据隐私保护的同时，提高模型协同训练的效率。其次，有针对性地设计了模型选择方法，并提出潜在恶意模型检测方法和车辆信誉评估方法，进一步增强系统鲁棒性。然后，从概率上详细分析了所提方法的安全性，为各项参数优化提供理论基础。最后，仿真结果表明该方法能够在实现高效异步联邦学习的同时具有较好的鲁棒性。

Abstract

The synchronous training mechanism of traditional federated learning was not suitable for dynamic vehicle computing power network scenarios

and lacked effective detection mechanisms under the threat of malicious vehicle attacks. To address the above issues

an asynchronous robust federated learning method was proposed

which achieves vehicle data privacy protection while improving the efficiency of model collaborative training through asynchronous execution of federated learning processes between vehicles. Secondly

a model selection method was designed

and potential malicious model detection and vehicle reputation evaluation methods are proposed to further enhance the robustness of the system. Then

the safety of the proposed method was analyzed in detail from a probabilistic perspective

providing a theoretical basis for optimizing various parameters. Finally

the simulation results show that this method can achieve efficient asynchronous federated learning while having good robustness.

关键词

Keywords

references

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