用联邦学习在多源卫星图像中实现隐私保护的船舶检测
Adaptive federated learning for ship detection across diverse satellite imagery sources
- 采用四种联邦学习模型，避免数据共享直接训练
- 检测精度显著高于本地小数据训练，接近全局训练水平
- 适配不同场景的船舶检测任务，尤其适合敏感数据
我们研究了联邦学习（FL）在多源卫星遥感图像中的船舶检测应用，提供了一种无需数据共享或集中收集的隐私保护方案，特别适用于商业卫星影像或敏感船舶标注。对比了四种联邦学习模型（FedAvg、FedProx、FedOpt、FedMedian）与本地训练基线，其中YOLOv8模型在各数据集上独立训练且不共享参数。结果表明，联邦学习模型在小规模本地数据上显著提升检测精度，性能接近使用全部数据进行全局训练的结果。研究强调了通信轮次和本地训练周期等配置对检测精度与计算效率的平衡作用。
原文摘要 · Abstract (English)
We investigate the application of Federated Learning (FL) for ship detection across diverse satellite datasets, offering a privacy-preserving solution that eliminates the need for data sharing or centralized collection. This approach is particularly advantageous for handling commercial satellite imagery or sensitive ship annotations. Four FL models including FedAvg, FedProx, FedOpt, and FedMedian, are evaluated and compared to a local training baseline, where the YOLOv8 ship detection model is independently trained on each dataset without sharing learned parameters. The results reveal that FL models substantially improve detection accuracy over training on smaller local datasets and achieve performance levels close to global training that uses all datasets during the training. Furthermore, the study underscores the importance of selecting appropriate FL configurations, such as the number of communication rounds and local training epochs, to optimize detection precision while maintaining computational efficiency.
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