Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

中文翻译：

---

使用联邦学习预测地震的框架

地震是一种自然现象，可能会导致生命和基础设施的重大损失。研究人员已经应用多种基于人工智能的技术来预测地震，但由于多维数据量大、通信延迟、传输延迟、处理能力有限和数据隐私问题，无法实现高精度。联合学习 (FL) 是一种机器学习 (ML) 技术，它提供了在不影响数据隐私和防止数据传输到中央服务器的情况下现场收集和处理数据的机会。通过聚合本地数据模型来获取全局数据模型的联合概念从本质上确保了数据安全性、数据隐私和数据异构性。在本文中，提出了一种使用FL的新型地震预测框架。所提出的 FL 框架在效率、可靠性和精度方面比已经开发的基于 ML 的地震预测模型具有更好的性能。我们分析了三个不同的本地数据集以生成多个基于 ML 的本地数据模型。这些本地数据模型已经使用 FedQuake 算法在中央 FL 服务器上聚合以生成全局数据模型。元分类器已在 FL 服务器上针对全局数据模型进行训练，以生成更准确的地震预测。我们通过分析喜马拉雅西部 34.708° N、72.5478° E 100 公里径向区域内的多维地震数据测试了所提出的框架。所提出框架的结果已根据过去 35 年仪器记录的区域地震数据进行了验证，已记录到 88.87% 的预测精度。