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

TL;DR

This paper presents FedUKD, a federated UNet model with knowledge distillation that enables efficient land use classification from satellite and street view images, reducing communication costs and enabling real-time environmental monitoring.

Contribution

The paper introduces a novel federated UNet architecture with integrated knowledge distillation for efficient, privacy-preserving land use classification from distributed image data.

Findings

Achieved over 95% accuracy in land use classification.

Significant model compression: 17x for street view, 62x for satellite images.

Reduced communication cost and response time in federated learning setup.

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 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.