Federated Learning-Based Approach for Landslide Forecasting in Taiwan

Landslides pose significant risks, often causing severe property damage and, in extreme cases, loss of life due to poorly timed evacuations. Accurate forecasting is, therefore, essential. Traditional landslide studies rely heavily on satellite imagery to analyze timing and impact, often using machine learning models to process these images or predict landslides based on relevant factors. However, the lack of sufficient data significantly compromises forecasting accuracy in data-scarce regions such as remote mountainous areas or highways. Federated learning, a cutting-edge machine learning paradigm, offers a promising solution by aggregating model parameters from decentralized edge models operating in different regions. This approach allows a central model to leverage diverse, region-specific data without requiring direct data sharing, resulting in a more robust and generalized predictive capability. The framework supports edge models that process localized data varying in both temporal and volumetric dimensions, while a carefully designed parameter aggregation mechanism ensures iterative improvement of the central model. Experimental results demonstrate that federated learning enhances forecasting performance and improves accuracy, particularly in regions with limited data availability, marking a significant step forward in landslide forecasting.