Federated Region-Learning: An Edge Computing Based Framework for Urban Environment Sensing

Sparse sensory data caused by insufficient monitoring sites and their incomplete records becomes the main challenge of fine-grained environment sensing. In this paper, we develop a novel inference framework, named Federated Region-Learning (FRL), for urban environment sensing. The proposed framework inherits the basic idea of federated learning, and also considers the regional characteristics during the distribution of training samples so as to improve the inference accuracy. Moreover, we exploit an edge computing architecture to implement the FRL for improving the computational efficiency. We also apply FRL to PM2.5 monitoring in Beijing. The evaluation shows that our FRL improves computational efficiency nearly 3 times than centralized training mode and increases accuracy by more than 5% compared with normal distributed training.