  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.
