Federated Learning in Clustered WSN for Natural Disaster Management
Zouheir Belfeki, Mondher Bouazizi, Moez Krichen, Salah Zidi · 2024
Federated Learning (FL) is a machine learning (ML) approach that allows a model to be trained across multiple decentralized devices holding local data samples without exchanging them. In the realm of Wireless Sensor Networks (WSNs), FL has not attracted much attention given that FL is typically meant to train models on data collected by much fewer and decently more powerful devices. However, given the potential of WSNs to collect diverse data in hazardous regions, we aim to explore how to employ FL to collect data in an area of interest where we have a natural disaster. In this paper, we introduce a novel task with regards to FL in the context of WSN for natural disaster management. Given a region where wireless sensors are deployed and data samples are distributed, we aim to cluster the sensors so that each cluster can be treated as a FL agent in a way that accelerates the process of FL.