Wildfire risk prediction has become critically important as climate change intensifies fire events worldwide. Wildfire datasets collected by different countries constitute a rich source of geographically and ecologically diverse information; however, data sovereignty regulations and privacy requirements substantially restrict the centralized aggregation of such data. This study examines the accuracy–privacy trade-off provided by federated learning (FL) when training a joint fire risk model across multiple countries' wildfire datasets without centralizing the raw data. Synthetic datasets representing five countries — Turkey, the United States, Australia, Portugal, and Canada — were used to compare FedAvg, FedProx, and differentially private federated learning algorithms. Experimental results demonstrate that federated learning models incur only a 2.3–4.7% accuracy loss compared to centralized training, while data privacy is mathematically guaranteed through differential privacy mechanisms. Furthermore, communication costs can be reduced by 60% through model compression techniques. The findings suggest that federated learning offers a viable solution for international wildfire risk modeling by overcoming data-sharing barriers while maintaining an acceptable accuracy–privacy balance.
Kaan Alper (Mon,) studied this question.