Federated Learning on Non-IID Environmental Images for Enhanced Wildfire Surveillance

Wildfires pose serious threats to ecosystems and human safety, requiring accurate monitoring systems. This study proposes a Federated Learning (FL) approach with Convolutional Neural Networks (CNNs) for wildfire detection using two heterogeneous image datasets, keeping the data locally on each client. The federated setup simulates non-Independent and identically distributed (IID) conditions, where each client trains locally and updates are aggregated its weights to a remote server. To ensure effective performance, hyperparameter optimization for each architecture was conducted using the Tree of Parzen Estimators (TPE), allowing efficient exploration of the best training configurations. Results demonstrate that FL can handle data heterogeneity while preserving privacy, with deeper CNN architectures achieving superior performance. The findings highlight the feasibility of FL for wildfire surveillance and the ability of optimized CNNs to generalize effectively across diverse environmental conditions, supporting collaborative model training without sharing raw data.