Federated Learning–Based Earthquake Forecasting in the Western Anatolia-Aegean Extensional Province

The Western Anatolia-Aegean region is dominated by active lithospheric extension, magmatism and widespread seismicity (including Samos earthquake Mw=7.0, 30.10. 2020). However, its complex tectonic setting including continuum/distributed mode of deformation rather than block type (more localized), and uneven station coverage highlight the limitations of traditional centralized machine-learning approaches. Notably, owing to data-sharing restrictions and the lack of regionally representative training datasets there are substantial challenges for developing reliable short-term earthquake forecasting models. To address these issues, we develop a federated learning (FL) framework that enables multiple seismic agencies and stations to collaboratively train predictive models without exchanging raw waveform data. Our dataset integrates multi-station acceleration and INSAR-GPS based displacement time-series, regional geological parameters, and spatiotemporal feature windows derived from AFAD, KOERI, IRIS, and USGS archives. Within this framework, we formulate two forecasting tasks: (i) classification of the likelihood of an earthquake exceeding a magnitude threshold within 24–72 hours, and (ii) regression-based estimation of short-term seismic intensity. Several deep-learning architectures, including 1D-CNN, LSTM, and CNN–LSTM hybrids, are implemented under both centralized and federated training schemes to systematically evaluate the effect of non-IID data distributions, communication constraints, and regional variability on forecasting skill. Comparative experiments show that FL preserves most of the predictive performance of centralized models while providing critical advantages in data privacy, scalability, and institutional participation. These results highlight the potential role of federated machine learning to support next-generation seismic forecasting systems, foster cross-institutional collaboration, and facilitate operational earthquake preparedness across data-restricted regions.