一种高效安全的基于CKKS的物联网数据处理自适应联邦学习方法

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yang Lan , Lixiang Li , Haipeng Peng , Yeqing Ren , Zhongkai Dang
{"title":"一种高效安全的基于CKKS的物联网数据处理自适应联邦学习方法","authors":"Yang Lan ,&nbsp;Lixiang Li ,&nbsp;Haipeng Peng ,&nbsp;Yeqing Ren ,&nbsp;Zhongkai Dang","doi":"10.1016/j.iot.2025.101725","DOIUrl":null,"url":null,"abstract":"<div><div>Federated learning (FL) provides a new paradigm for solving the security of private data in the internet of things (IoT). However, the huge consumption of computing resources and communication cost makes the FL process inefficient. To solve above problems, this paper proposes an efficient and secure adaptive federated learning method based on CKKS homomorphic encryption (HE) for data processing in IoT. Inspired by dropout, we propose an adaptive inactivation of weights strategy. Through adaptive change of inactivation parameter, part of the weights after reorganization are encrypted and uploaded in each communication. The dual protection of reorganization operation and HE can better protect the weights information. Then, to alleviate the impact of the above methods on the performance of FL, the local data distribution and the change of model accuracy are considered, we propose the federated aggregation method with reward and punishment factor, and the historical information of the local model is employed to design a weights correction strategy. Finally, we use MNIST dataset, fashion-MNIST dataset, GTSRB dataset and CSE-CIC-IDS2018 dataset to design non-independent and identically distributed data scenarios, and a large number of experiments are carried out to verify the effectiveness of the proposed method. Our method not only protects the privacy of weights information, but also reduces the communication cost and the local resource consumption caused by the encryption, which provides a good reference for the follow-up development of FL in IoT.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101725"},"PeriodicalIF":7.6000,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An efficient and secure adaptive federated learning method based on CKKS for data processing in the Internet of Things\",\"authors\":\"Yang Lan ,&nbsp;Lixiang Li ,&nbsp;Haipeng Peng ,&nbsp;Yeqing Ren ,&nbsp;Zhongkai Dang\",\"doi\":\"10.1016/j.iot.2025.101725\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Federated learning (FL) provides a new paradigm for solving the security of private data in the internet of things (IoT). However, the huge consumption of computing resources and communication cost makes the FL process inefficient. To solve above problems, this paper proposes an efficient and secure adaptive federated learning method based on CKKS homomorphic encryption (HE) for data processing in IoT. Inspired by dropout, we propose an adaptive inactivation of weights strategy. Through adaptive change of inactivation parameter, part of the weights after reorganization are encrypted and uploaded in each communication. The dual protection of reorganization operation and HE can better protect the weights information. Then, to alleviate the impact of the above methods on the performance of FL, the local data distribution and the change of model accuracy are considered, we propose the federated aggregation method with reward and punishment factor, and the historical information of the local model is employed to design a weights correction strategy. Finally, we use MNIST dataset, fashion-MNIST dataset, GTSRB dataset and CSE-CIC-IDS2018 dataset to design non-independent and identically distributed data scenarios, and a large number of experiments are carried out to verify the effectiveness of the proposed method. Our method not only protects the privacy of weights information, but also reduces the communication cost and the local resource consumption caused by the encryption, which provides a good reference for the follow-up development of FL in IoT.</div></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":\"33 \",\"pages\":\"Article 101725\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2542660525002392\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660525002392","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

联邦学习(FL)为解决物联网(IoT)中私有数据的安全问题提供了一种新的范式。然而,巨大的计算资源消耗和通信成本使得FL过程效率低下。针对上述问题,本文提出了一种高效、安全的基于CKKS同态加密(HE)的物联网数据处理自适应联邦学习方法。受dropout的启发,我们提出了一种自适应权值失活策略。通过自适应改变失活参数,在每次通信中对重组后的部分权值进行加密上传。重组操作和HE的双重保护能更好地保护权重信息。然后,考虑到局部数据分布和模型精度变化的影响,提出了带有奖惩因子的联邦聚合方法,并利用局部模型的历史信息设计了权重修正策略。最后,我们使用MNIST数据集、fashion-MNIST数据集、GTSRB数据集和CSE-CIC-IDS2018数据集设计了非独立且同分布的数据场景,并进行了大量实验验证了所提方法的有效性。我们的方法既保护了权重信息的隐私性,又降低了通信成本和加密带来的本地资源消耗,为物联网中FL的后续发展提供了很好的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient and secure adaptive federated learning method based on CKKS for data processing in the Internet of Things
Federated learning (FL) provides a new paradigm for solving the security of private data in the internet of things (IoT). However, the huge consumption of computing resources and communication cost makes the FL process inefficient. To solve above problems, this paper proposes an efficient and secure adaptive federated learning method based on CKKS homomorphic encryption (HE) for data processing in IoT. Inspired by dropout, we propose an adaptive inactivation of weights strategy. Through adaptive change of inactivation parameter, part of the weights after reorganization are encrypted and uploaded in each communication. The dual protection of reorganization operation and HE can better protect the weights information. Then, to alleviate the impact of the above methods on the performance of FL, the local data distribution and the change of model accuracy are considered, we propose the federated aggregation method with reward and punishment factor, and the historical information of the local model is employed to design a weights correction strategy. Finally, we use MNIST dataset, fashion-MNIST dataset, GTSRB dataset and CSE-CIC-IDS2018 dataset to design non-independent and identically distributed data scenarios, and a large number of experiments are carried out to verify the effectiveness of the proposed method. Our method not only protects the privacy of weights information, but also reduces the communication cost and the local resource consumption caused by the encryption, which provides a good reference for the follow-up development of FL in IoT.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信