Double layer federated security learning architecture for artificial intelligence of things
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. I...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2024-12-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024081 |
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| _version_ | 1823864895932727296 |
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| author | ZHENG Chengbo YAN Haonan FU Caili ZHANG Dong LI Hui WANG Bin |
| author_facet | ZHENG Chengbo YAN Haonan FU Caili ZHANG Dong LI Hui WANG Bin |
| author_sort | ZHENG Chengbo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9810046c9be34fd690f6be8506d9b313 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-9810046c9be34fd690f6be8506d9b3132025-02-08T19:00:08ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2024-12-0110718080361644Double layer federated security learning architecture for artificial intelligence of thingsZHENG ChengboYAN HaonanFU CailiZHANG DongLI HuiWANG BinFederated 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.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024081artificial intelligence of thingsfederated learningpoisoning attacksecure multi-party computationblockchainprivacy protection |
| spellingShingle | ZHENG Chengbo YAN Haonan FU Caili ZHANG Dong LI Hui WANG Bin Double layer federated security learning architecture for artificial intelligence of things 网络与信息安全学报 artificial intelligence of things federated learning poisoning attack secure multi-party computation blockchain privacy protection |
| title | Double layer federated security learning architecture for artificial intelligence of things |
| title_full | Double layer federated security learning architecture for artificial intelligence of things |
| title_fullStr | Double layer federated security learning architecture for artificial intelligence of things |
| title_full_unstemmed | Double layer federated security learning architecture for artificial intelligence of things |
| title_short | Double layer federated security learning architecture for artificial intelligence of things |
| title_sort | double layer federated security learning architecture for artificial intelligence of things |
| topic | artificial intelligence of things federated learning poisoning attack secure multi-party computation blockchain privacy protection |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024081 |
| work_keys_str_mv | AT zhengchengbo doublelayerfederatedsecuritylearningarchitectureforartificialintelligenceofthings AT yanhaonan doublelayerfederatedsecuritylearningarchitectureforartificialintelligenceofthings AT fucaili doublelayerfederatedsecuritylearningarchitectureforartificialintelligenceofthings AT zhangdong doublelayerfederatedsecuritylearningarchitectureforartificialintelligenceofthings AT lihui doublelayerfederatedsecuritylearningarchitectureforartificialintelligenceofthings AT wangbin doublelayerfederatedsecuritylearningarchitectureforartificialintelligenceofthings |