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

• URL: http://arxiv.org/abs/2212.02196v1
• Date: Mon, 5 Dec 2022 12:14:00 GMT
• Title: FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views
• Authors: Renuga Kanagavelu, Kinshuk Dua, Pratik Garai, Susan Elias, Neha Thomas, Simon Elias, Qingsong Wei, Goh Siow Mong Rick, Liu Yong
• Abstract summary: We use a Federated UNet model for Semantic of satellite and street view images. The accuracy 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.
• Score: 1.7847474813778919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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