Stable federated learning method for low-altitude IoT networks based on election strategy
The deep integration of UAV and Internet of things (IoT) transmits a large amount of sensitive data in the air-to-ground intelligent network, posing a serious risk of privacy leakage. The proposal of federated learning (FL) provides a privacy-preserving solution for low-altitude IoT applications, al...
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2024-09-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00415/ |
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| _version_ | 1850034584396234752 |
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| author | SHEN Lingfeng WANG Guanghui BAI Tianshui ZHU Zhengyu ZHANG Qiankun |
| author_facet | SHEN Lingfeng WANG Guanghui BAI Tianshui ZHU Zhengyu ZHANG Qiankun |
| author_sort | SHEN Lingfeng |
| collection | DOAJ |
| description | The deep integration of UAV and Internet of things (IoT) transmits a large amount of sensitive data in the air-to-ground intelligent network, posing a serious risk of privacy leakage. The proposal of federated learning (FL) provides a privacy-preserving solution for low-altitude IoT applications, allowing multiple participants to jointly train models without sharing sensitive data. However, the federated learning performance is unstable because of various application scenarios, heterogeneous nodes and dynamic environments. An federated fearning based on proxy Raft election and weight calculation (FedREP-W) method was proposed, which combined classical Raft election and weight calculation, significantly improving the stability and efficiency of federated training. To be more specific, the use of Raft to choose new agent devices keeped federated learning stable. By incorporating the concept of weight elections, the effectiveness of federated learning could be enhenced by designating the most powerful node as an agent. The experimental results publicly available datasets show that the proposed strategy and algorithm perform well in lowering the number of communication rounds, speeding up model convergence, and making the system stable. This provides a feasible solution for efficient, secure, and stable federated learning in low-altitude IoT networks. |
| format | Article |
| id | doaj-art-c137945727d845f8982f2faa752296fe |
| institution | DOAJ |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-c137945727d845f8982f2faa752296fe2025-08-20T02:57:46ZzhoChina InfoCom Media Group物联网学报2096-37502024-09-018556577121100Stable federated learning method for low-altitude IoT networks based on election strategySHEN LingfengWANG GuanghuiBAI TianshuiZHU ZhengyuZHANG QiankunThe deep integration of UAV and Internet of things (IoT) transmits a large amount of sensitive data in the air-to-ground intelligent network, posing a serious risk of privacy leakage. The proposal of federated learning (FL) provides a privacy-preserving solution for low-altitude IoT applications, allowing multiple participants to jointly train models without sharing sensitive data. However, the federated learning performance is unstable because of various application scenarios, heterogeneous nodes and dynamic environments. An federated fearning based on proxy Raft election and weight calculation (FedREP-W) method was proposed, which combined classical Raft election and weight calculation, significantly improving the stability and efficiency of federated training. To be more specific, the use of Raft to choose new agent devices keeped federated learning stable. By incorporating the concept of weight elections, the effectiveness of federated learning could be enhenced by designating the most powerful node as an agent. The experimental results publicly available datasets show that the proposed strategy and algorithm perform well in lowering the number of communication rounds, speeding up model convergence, and making the system stable. This provides a feasible solution for efficient, secure, and stable federated learning in low-altitude IoT networks.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00415/low-altitude IoTfederated learningdevice election strategystabilitytraining efficiency |
| spellingShingle | SHEN Lingfeng WANG Guanghui BAI Tianshui ZHU Zhengyu ZHANG Qiankun Stable federated learning method for low-altitude IoT networks based on election strategy 物联网学报 low-altitude IoT federated learning device election strategy stability training efficiency |
| title | Stable federated learning method for low-altitude IoT networks based on election strategy |
| title_full | Stable federated learning method for low-altitude IoT networks based on election strategy |
| title_fullStr | Stable federated learning method for low-altitude IoT networks based on election strategy |
| title_full_unstemmed | Stable federated learning method for low-altitude IoT networks based on election strategy |
| title_short | Stable federated learning method for low-altitude IoT networks based on election strategy |
| title_sort | stable federated learning method for low altitude iot networks based on election strategy |
| topic | low-altitude IoT federated learning device election strategy stability training efficiency |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00415/ |
| work_keys_str_mv | AT shenlingfeng stablefederatedlearningmethodforlowaltitudeiotnetworksbasedonelectionstrategy AT wangguanghui stablefederatedlearningmethodforlowaltitudeiotnetworksbasedonelectionstrategy AT baitianshui stablefederatedlearningmethodforlowaltitudeiotnetworksbasedonelectionstrategy AT zhuzhengyu stablefederatedlearningmethodforlowaltitudeiotnetworksbasedonelectionstrategy AT zhangqiankun stablefederatedlearningmethodforlowaltitudeiotnetworksbasedonelectionstrategy |