用联邦学习检测森林砍伐，保护数据隐私同时提升识别精度。
Federated Learning for Deforestation Detection: A Distributed Approach with Satellite Imagery
- 通过联邦学习在多卫星边缘中心协作训练模型，不交换原始数据。
- 在公开数据集上使用YOLO-Small、Faster R-CNN等模型实现高精度定位。
- 适合关注遥感图像分析与隐私保护的科研及环保机构使用。
从卫星图像中准确识别森林砍伐对了解区域地理状况至关重要。本文提出一种基于联邦学习（FL）的分布式方法，用于在不同客户端间识别并定位森林砍伐。每个客户端对应一个负责本地数据处理的边缘卫星中心。联邦学习使各客户端在不共享原始数据的前提下协同训练模型，避免了集中式训练需合并数据带来的隐私风险。框架基于FLOWER与RAY实现分布式计算，利用RAY高效生成客户端模拟环境。实验采用YOLOS-small（视觉变换器变体）、带有ResNet50主干的Faster R-CNN以及带有MobileNetV3主干的Faster R-CNN，在公开数据集上进行训练与测试。该方法为卫星影像上的图像分割任务提供了新视角。
原文摘要 · Abstract (English)
Accurate identification of deforestation from satellite images is essential in order to understand the geographical situation of an area. This paper introduces a new distributed approach to identify as well as locate deforestation across different clients using Federated Learning (FL). Federated Learning enables distributed network clients to collaboratively train a model while maintaining data privacy and security of the active users. In our framework, a client corresponds to an edge satellite center responsible for local data processing. Moreover, FL provides an advantage over centralized training method which requires combining data, thereby compromising with data security of the clients. Our framework leverages the FLOWER framework with RAY framework to execute the distributed learning workload. Furthermore, efficient client spawning is ensured by RAY as it can select definite amount of users to create an emulation environment. Our FL framework uses YOLOS-small (a Vision Transformer variant), Faster R-CNN with a ResNet50 backbone, and Faster R-CNN with a MobileNetV3 backbone models trained and tested on publicly available datasets. Our approach provides us a different view for image segmentation-based tasks on satellite imagery.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。