网络与信息安全学报2024，Vol.10Issue(6)：71-80,10.DOI:10.11959/j.issn.2096-109x.2024081
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
摘要
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 in-telligent 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 decen-tralized security aggregation with gradient privacy protection within linear time complexity.关键词
智能物联网/联邦学习/投毒攻击/安全多方计算/区块链/隐私保护Key words
artificial intelligence of things/federated learning/poisoning attack/secure multi-party computation/blockchain/privacy protection引用本文复制引用
郑诚波,闫皓楠,傅彩利,张栋,李晖,王滨..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):71-80,10.基金项目
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) The National Natural Science Foundation of China(92167203,61932015,62402373),The China Postdoc-toral Science Foundation(374102),The Zhejiang Province Postdoctoral Research Priority Funding Program(ZJ2024009) （92167203,61932015,62402373）
