Federated Learning for Deforestation Detection: A Distributed Approach with Satellite Imagery
This work addresses deforestation monitoring for environmental agencies by enabling collaborative model training across distributed satellite centers while preserving data privacy, though it is incremental as it applies existing FL methods to a specific domain.
The paper tackles deforestation detection from satellite imagery by introducing a distributed approach using Federated Learning (FL) to train models like YOLOS-small and Faster R-CNN variants, achieving results that offer a new perspective for image segmentation tasks.
Accurate identification of deforestation from satellite images is essential in order to understand the geographical situation of an area. This paper introduces a new distributed approach to identify as well as locate deforestation across different clients using Federated Learning (FL). Federated Learning enables distributed network clients to collaboratively train a model while maintaining data privacy and security of the active users. In our framework, a client corresponds to an edge satellite center responsible for local data processing. Moreover, FL provides an advantage over centralized training method which requires combining data, thereby compromising with data security of the clients. Our framework leverages the FLOWER framework with RAY framework to execute the distributed learning workload. Furthermore, efficient client spawning is ensured by RAY as it can select definite amount of users to create an emulation environment. Our FL framework uses YOLOS-small (a Vision Transformer variant), Faster R-CNN with a ResNet50 backbone, and Faster R-CNN with a MobileNetV3 backbone models trained and tested on publicly available datasets. Our approach provides us a different view for image segmentation-based tasks on satellite imagery.