论文标题
FEDUKD：联合UNET模型，具有知识蒸馏的土地利用分类，可从卫星和街道景观
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views
论文作者
论文摘要
联合深度学习框架可以战略性地使用本地监测土地使用，并在全球范围内推断环境影响。将需要来自世界各地的分布式数据来建立用于土地使用分类的全球模型。在此应用程序域中需要联合方法的需求是避免从分布式位置传输数据并节省网络带宽以降低通信成本。我们使用联合的UNET模型进行卫星和街景图像的语义细分。所提出的体系结构的新颖性是知识蒸馏以减少沟通成本和响应时间的整合。所获得的精度高于95％，我们还将明显的模型压缩到了街道视图和卫星图像的17次和62次以上。我们提出的框架有可能成为改变地球气候变化的实时跟踪游戏改变游戏的可能性。
Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for Land Use classification. The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost. We use a Federated UNet model for Semantic Segmentation of satellite and street view images. The novelty of the proposed architecture is the integration of Knowledge Distillation to reduce communication cost and response time. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street View and satellite images respectively. Our proposed framework has the potential to be a game-changer in real-time tracking of climate change across the planet.
