FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views

Abstract

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 the transfer of data from distributed locations and save network bandwidth to reduce communication costs. We used a federated UNet model for the semantic segmentation of satellite and street view images. The novelty of the proposed architecture involves the integration of knowledge distillation to reduce communication costs and response times. 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 significantly improve the efficiency and privacy of real-time tracking of climate change across the planet.

FKD-Med: Privacy-Aware, Communication-Optimized Medical Image Segmentation via Federated Learning and Model Lightweighting Through Knowledge Distillation

“…It details how FKD-Med applies FL and KD in its ResUnet and TransUnet variants to achieve marked parameter optimization. Compared to models such as U-net, Spirit Distill [42], ContextNet [43], MKANet [44], and FedUKD [15], FKD-Med(ResUnet) demonstrates a 127-fold increase in parameter efficiency, and FKD-Med(TransUnet) achieves an even more impressive 1027-fold enhancement. These figures highlight FKD-Med's significant strides in optimizing communication efficiency and reinforcing data privacy.…”

“…Meanwhile, FedX demonstrated unsupervised FL with two-sided KD, significantly improving performance across various unsupervised algorithms [14]. Finally, FedUKD integrated KD into a federated UNet model for land use classification from satellite and street view images, resulting in significant model compression and accuracy [15].…”

“…FedX introduced unsupervised learning with dual-sided KD, boosting performance in unsupervised algorithms [14]. Additionally, Some research combined KD with a federated UNet for land use classification, achieving notable model compression and accuracy improvements [15]. However, there is a notable gap in the development of framework tools for the integration of Federated Learning (FL) and Knowledge Distillation (KD) specifically tailored to medical image segmentation.…”

An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection

“…Jin et al [16] used the federated-learning framework for network traffic classification. Kanagavelu et al [17] used the federated UNet model to semantically segment satellite images and street view images, and they finally realized a land use classification. Lv et al [18] constructed a federated random forest algorithm to realize the classification of ship AIS trajectory data and added a homomorphic encryption process to protect the data in the federated process.…”