面向智能物联网的双层级联邦安全学习架构

郑诚波; 闫皓楠; 傅彩利; 张栋; 李晖; 王滨

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

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面向智能物联网的双层级联邦安全学习架构

学术论文 | 更新时间：2025-02-06

- 面向智能物联网的双层级联邦安全学习架构
- Double layer federated security learning architecture for artificial intelligence of things
- 网络与信息安全学报 2024年10卷第6期页码：71-80
- 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：
  
  [ "郑诚波（1999- ），男，江苏南通人，西安电子科技大学硕士生，主要研究方向为联邦学习、人工智能安全。" ]
  [ "闫皓楠（1996- ），男，山西忻州人，西安电子科技大学博士生，主要研究方向为可信人工智能、隐私计算。" ]
  [ "傅彩利（1986- ），男，浙江杭州人，杭州海康威视数字技术股份有限公司高级工程师，主要研究方向为物联网安全。" ]
  [ "张栋（1991- ），男，河北衡水人，国家计算机网络应急技术处理协调中心工程师，主要研究方向为人工智能安全、区块链、下一代互联网。" ]
  [ "李晖（1968- ），男，河南灵宝人，西安电子科技大学教授、博士生导师，主要研究方向为密码信息安全、隐私计算、信息论与编码理论。" ]
  [ "王滨（1978- ），男，山东泗水人，浙江省全省智能物联网络与数据安全重点实验室研究员，主要研究方向为物联网安全、人工智能安全、密码学。" ]
- 基金信息：
  
  国家自然科学基金(92167203;61932015;62402373);中国博士后科学基金(374102);浙江省博士后科研择优项目(ZJ2024009)
- DOI：10.11959/j.issn.2096-109x.2024081
  中图分类号：
- 收稿：2024-04-28，
  
  修回：2024-09-30，
  
  纸质出版：2024-12-25
- 稿件说明：
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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.

关键词

Keywords

references

LIU B Y , YAN B J , ZHOU Y Z , et al . Experiments of federated learning for COVID-19 chest X-ray images [EB ] . arXiv Preprint arXiv: 2007.05592 , 2020 .

SHELLER M J , REINA G A , EDWARDS B , et al . Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation [J ] . Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries BrainLes , 2019 , 11383 : 92 - 104 .

CHEN Y Q , QIN X , WANG J D , et al . FedHealth: a federated transfer learning framework for wearable healthcare [J ] . IEEE Intelligent Systems , 2020 , 35 ( 4 ): 83 - 93 .

ARIVAZHAGAN M G , AGGARWAL V , SINGH A K , et al . Federated learning with personalization layers [EB ] . arXiv Preprint arXiv: 1912.00818 , 2019 .

SHELLER M J , EDWARDS B , REINA G A , et al . Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data [J ] . Scientific Reports , 2020 , 10 : 12598 .

SAPUTRA Y M , HOANG D T , NGUYEN D N , et al . Energy demand prediction with federated learning for electric vehicle networks [C ] // Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE Press , 2019 : 1 - 6 .

高莹 , 陈晓峰 , 张一余 , 等 . 联邦学习系统攻击与防御技术研究综述 [J ] . 计算机学报 , 2023 , 46 ( 9 ): 1781 - 1805 .

GAO Y , CHEN X F , ZHANG Y Y , et al . A survey of attack and defense techniques for federated learning systems [J ] . Chinese Journal of Computers , 2023 , 46 ( 9 ): 1781 - 1805 .

ZHU L , LIU Z , HAN S . Deep leakage from gradients [C ] // Proceedings of Advances in Neural Information Processing Systems , 2019 , 32 .

BLANCHARD P , EL MHAMDI E M , GUERRAOUI R , et al . Machine learning with adversaries: Byzantine tolerant gradient descent [C ] // Proceedings of Advances in Neural Information Processing Systems , 2017 , 30 .

XIE C , KOYEJO O , GUPTA I . Generalized byzantine-tolerant SGD [EB ] . arXiv preprint arXiv: 1802.10116 , 2018 .

YIN D , CHEN Y D , RAMCHANDRAN K , et al . Byzantine-robust distributed learning: towards optimal statistical rates [EB ] . arXiv Preprint arXiv: 1803.01498 , 2018 .

CHEN Y D , SU L L , XU J M . Distributed statistical machine learning in adversarial settings [J ] . Proceedings of the ACM on Measurement and Analysis of Computing Systems , 2017 , 1 ( 2 ): 1 - 25 .

ALISTARH D , ALLEN-ZHU Z , LI J . Byzantine stochastic gradient descent [EB ] . arXiv Preprint arXiv: 1803.08917 , 2018 .

XIA Q , TAO Z Y , HAO Z J , et al . FABA: an algorithm for fast aggregation against Byzantine attacks in distributed neural networks [C ] // Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence . California : International Joint Conferences on Artificial Intelligence Organization , 2019 .

PRAKASH S , AVESTIMEHR A S . Mitigating byzantine attacks in federated learning [EB ] . arXiv Preprint arXiv: 2010.07541 , 2020 .

XIA G M , CHEN J , YU C D , et al . Poisoning attacks in federated learning: a survey [J ] . IEEE Access , 2023 , 11 : 10708 - 10722 .

MUÑOZ-GONZÁLEZ L , CO K T , LUPU E C . Byzantine-robust federated machine learning through adaptive model averaging [EB ] . arXiv Preprint arXiv: 1909.05125 , 2019 .

CAO X Y , LAI L F . Distributed gradient descent algorithm robust to an arbitrary number of Byzantine attackers [J ] . IEEE Transactions on Signal Processing , 2019 , 67 ( 22 ): 5850 - 5864 .

YAN H , ZHANG W , CHEN Q , et al . Recess vaccine for federated learning: proactive defense against model poisoning attacks [C ] // Proceedings of Advances in Neural Information Processing Systems , 2024 , 36 .

DWORK C . Differential privacy automata, languages and programming [M ] . Berlin, Heidelberg : Springer Berlin Heidelberg , 2006 : 1 - 12 .

李敏 , 肖迪 , 陈律君 . 兼顾通信效率与效用的自适应高斯差分隐私个性化联邦学习 [J ] . 计算机学报 , 2024 , 47 ( 4 ): 924 - 946 .

LI M , XIAO D , CHEN L . Adaptive Gaussian differential privacy personalized federated learning considering communication efficiency and utility [J ] . Chinese Journal of Computers , 2024 , 47 ( 4 ): 924 - 946 .

ACAR A , AKSU H , ULUAGAC A S , et al . A survey on homomorphic encryption schemes [J ] . ACM Computing Surveys , 2019 , 51 ( 4 ): 1 - 35 .

CHEN Q , WANG Z L , CHEN J W , et al . Dap-FL: federated learning Flourishes by adaptive tuning and secure aggregation [J ] . IEEE Transactions on Parallel and Distributed Systems , 2023 , 34 ( 6 ): 1923 - 1941 .

TRUEX S , BARACALDO N , ANWAR A , et al . A hybrid approach to privacy-preserving federated learning [C ] // Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security . 2019 : 1 - 11 .

肖雄 , 唐卓 , 肖斌 , 等 . 联邦学习的隐私保护与安全防御研究综述 [J ] . 计算机学报 , 2023 , 46 ( 5 ): 1019 - 1044 .

XIAO X , TANG Z , XIAO B , et al . A survey on privacy and security issues in federated learning [J ] . Chinese Journal of Computers , 2023 , 46 ( 5 ): 1019 - 1044 .

AHMED M , SERAJ R , ISLAM S M S . The k-means algorithm: a comprehensive survey and performance evaluation [J ] . Electronics , 2020 , 9 ( 8 ): 1295 .

TIBSHIRANI R , WALTHER G , HASTIE T . Estimating the number of clusters in a data set via the gap statistic [J ] . Journal of the Royal Statistical Society Series B: Statistical Methodology , 2001 , 63 ( 2 ): 411 - 423 .

MCMAHAN H B , MOORE E , RAMAGE D , et al . Communication-efficient learning of deep networks from decentralized data [EB ] . arXiv Preprint arXiv: 1602.05629 , 2016 .

FANG M , CAO X , JIA J , et al . Local model poisoning attacks to byzantine-robust federated learning [C ] // Proceedings of the 29th Usenix Conference on Security Symposium . 2020 : 1623 - 1640 .

XIE C , HUANG K , CHEN P Y , et al . DBA: distributed backdoor attacks against federated learning [C ] // Proceedings of International Conference on Learning Representations . 2019 .

BAGDASARYAN E , VEIT A , HUA Y Q , et al . How to backdoor federated learning [EB ] . arXiv Preprint arXiv: 1807.00459 , 2018 .

BARUCH M , BARUCH G , GOLDBERG Y . A little is enough: circumventing defenses for distributed learning [EB ] . arXiv Preprint arXiv: 1902.06156 , 2019 .

YIN D , CHEN Y D , RAMCHANDRAN K , et al . Byzantine-Robust distributed learning: towards optimal statistical rates [EB ] . arXiv Preprint arXiv: 1803.01498 , 2018 .

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