Double layer federated security learning architecture for artificial intelligence of things

ZHENG Chengbo; YAN Haonan; FU Caili; ZHANG Dong; LI Hui; WANG Bin

doi:10.11959/j.issn.2096-109x.2024081

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Double layer federated security learning architecture for artificial intelligence of things

Papers | 更新时间：2025-02-06

- Double layer federated security learning architecture for artificial intelligence of things
- Chinese Journal of Network and Information Security Vol. 10, Issue 6, Pages: 71-80(2024)
- 作者机构：
  
  1.西安电子科技大学网络与信息安全学院，陕西西安 710126
  2.杭州海康威视数字技术股份有限公司，浙江杭州 310051
  3.国家计算机网络应急技术处理协调中心，北京 100029
  4.浙江省全省智能物联网络与数据安全重点实验室，浙江杭州 310051
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(92167203;61932015;62402373);The China Postdoctoral Science Foundation(374102);The Zhejiang Province Postdoctoral Research Priority Funding Program(ZJ2024009)
- DOI：10.11959/j.issn.2096-109x.2024081
  CLC：
- Received：28 April 2024，
  
  Revised：2024-09-30，
  
  Published：25 December 2024
- 稿件说明：
移动端阅览
郑诚波, 闫皓楠, 傅彩利, 等. 面向智能物联网的双层级联邦安全学习架构[J]. 网络与信息安全学报, 2024, 10(6): 71-80.

ZHENG C B, YAN H N, FU C L, et al. Double layer federated security learning architecture for artificial intelligence of things[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 71-80.
郑诚波, 闫皓楠, 傅彩利, 等. 面向智能物联网的双层级联邦安全学习架构[J]. 网络与信息安全学报, 2024, 10(6): 71-80. DOI： 10.11959/j.issn.2096-109x.2024081.

ZHENG C B, YAN H N, FU C L, et al. Double layer federated security learning architecture for artificial intelligence of things[J]. Chinese Journal of Network and Information Security, 2024, 10(6): 71-80. DOI： 10.11959/j.issn.2096-109x.2024081.

摘要

联邦学习作为一种分布式机器学习架构，可在保护数据隐私的条件下完成模型共同训练，被广泛应用于智能物联网中。然而，联邦学习中往往存在隐私泄露、投毒攻击等安全威胁。为了克服智能物联网场景下，多机构间使用联邦学习进行联合训练时存在的性能和安全的挑战，提出了面向智能物联网的双层级联邦安全学习架构。该架构将整个智能物联网安全学习系统分为底层和顶层的双层级架构，底层架构由机构内各个物联网设备和一台服务器组成，不同物联网设备之间通过区块链网络连接，服务器通过设备上传的历史梯度进行投毒攻击检测并剔除恶意设备，避免投毒攻击造成的收敛缓慢、全局模型精度下降等问题；顶层架构由不同机构的服务器组成，采用基于秘密共享的安全多方计算进行安全聚合，保护梯度隐私的同时实现了去中心化聚合。实验结果表明，该架构对4种常见投毒攻击的检测准确率均达到85%以上，极大地提高了系统的安全性，并在线性时间复杂度内实现梯度隐私保护的去中心化安全聚合。

Abstract

Federated learning

as a distributed machine learning architecture

can complete model co-training while protecting data privacy

and is widely used in Artificial Intelligence of Things. However

there are often security threats such as privacy breaches and poisoning attacks in federated learning. In order to overcome the performance and security challenges of using federated learning for joint training among multiple institutions in the context of intelligent Internet of Things

a two-level federated security learning architecture was proposed for intelligent Internet of Things. The entire security learning system was divided into a two-level architecture of bottom and top layers. The bottom architecture consisted of various IoT devices and a server within the organization. Different devices were connected through blockchain networks

and the server detected and eliminated malicious devices through the historical gradient uploaded by the devices

avoiding slow convergence and decreased global model accuracy caused by poisoning attacks. The top-level architecture consisted of servers from different institutions

using secure multi-party computation based on secret sharing for secure aggregation

protecting gradient privacy while achieving decentralized gradient aggregation. The experimental results show that the architecture achieves detection accuracy of over 85% for four common poisoning attacks

greatly improving the security of the system and achieving decentralized security aggregation with gradient privacy protection within linear time complexity.

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