Liu Siyang, Shan Nanliang, Bao Xianqiang, Xu Xinghua
National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China.
East Lake Laboratory, Wuhan 430202, China.
Sensors (Basel). 2025 Aug 1;25(15):4752. doi: 10.3390/s25154752.
Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing's near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified.
无人机、无人地面车辆和自主水下航行器等无人平台在执行协同数据处理任务时，常常面临数据、设备和模型异构性的挑战。现有研究并未同时从这三个方面解决问题。为解决此问题，本研究设计了一种受云边端模型启发的无人平台集群架构。该架构集成了用于隐私保护的联邦学习，利用分布式模型训练的优势，并运用边缘计算的近源数据处理能力。此外，本文提出了一种联邦边缘智能方法（DSIA-FEI），它包含两个关键组件。基于传统联邦学习，引入了一种数据共享机制，即从边缘侧平台提取数据并放入数据共享平台以形成公共数据集。在模型训练开始时，从公共数据集中进行随机采样并分发给每个无人平台，以减轻无人平台协同数据处理过程中数据分布异构性和类别不平衡的影响。此外，还开发了一种基于相似度度量和损失梯度的智能模型聚合策略。该策略通过分层参数对齐将异构模型参数映射到统一空间，并实时评估边缘设备的局部模型与全局模型之间的相似度以及损失梯度，以选择用于全局聚合的最优模型，减少设备和模型异构性对无人平台集群协同学习的影响。本研究在多个数据集上进行了广泛验证，实验结果表明，本文提出的DSIA-FEI在FEMNIST、FEAIR、EuroSAT和RSSCN7数据集上的准确率分别达到0.91、0.91、0.88和0.87，比基线方法高出10%以上。此外，通信轮数减少了40%以上，优于现有主流方法，验证了所提方法的有效性。
