文献类型：期刊文献

英文题名：Bc²FL: Double-Layer Blockchain-Driven Federated Learning Framework for Agricultural IoT

作者：Ding, Qingyang[1];Yue, Xiaofei[2];Zhang, Qinnan[3];Xiong, Zehui[4];Chang, Jinping[1];Zheng, Hongwei[5,6]

第一作者：Ding, Qingyang

通讯作者：Yue, XF[1];Zhang, QN[2]

机构：[1]Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China;[2]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China;[3]Beihang Univ, Inst Artificial Intelligence, Beijing Adv Innovat Ctr Future Blockchain & Privac, Beijing 100191, Peoples R China;[4]Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore 487372, Singapore;[5]Beijing Acad Blockchain & Edge Comp, Blockchain & Privacy Comp Technol R&D Dept, Beijing 100190, Peoples R China;[6]Beihang Univ, Beijing Adv Innovat Ctr Future Blockchain & Privac, Beijing 100191, Peoples R China

第一机构：北京联合大学管理学院

通讯机构：[1]corresponding author), Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China;[2]corresponding author), Beihang Univ, Inst Artificial Intelligence, Beijing Adv Innovat Ctr Future Blockchain & Privac, Beijing 100191, Peoples R China.

年份：2025

卷号：12

期号：4

起止页码：4362-4374

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001416193400031)】；

基金：This work was supported in part by the Research and Development Program of Beijing Municipal Education Commission under Grant KM202211417008; in part by the Beijing Social Science Foundation under Grant 23GLC037; in part by the Beijing Natural Science Foundation under Grant 9222012; in part by the Education Science Planning Project of Beijing Union University under Grant JK202212; and in part by the National Key Research and Development Program of China under Grant 2022YFC3300804.

语种：英文

外文关键词：Blockchains; Internet of Things; Privacy; Computational modeling; Federated learning; Data models; Biological system modeling; Adaptation models; Accuracy; Servers; Agriculture IoT; blockchain; federated learning (FL); knowledge-sharing; privacy protection

摘要：With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we presentBc(2)FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc(2)FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc(2)FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.