联邦学习用于全球碳排放预测：一种结合统计与神经模型的混合时间序列方法
Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models
AI总结：
提出联邦混合预测框架，集成ARIMA、GARCH、LSTM-Attention和XGBoost，在保护隐私下实现分布式碳排放预测，在14个客户上平均R²为0.73。
摘要：
气候变化主要由二氧化碳（CO2）排放驱动，需要准确的预测工具来支持有效的减缓政策和可持续发展战略。现有的预测方法通常依赖于集中式数据收集，而这往往受到隐私法规以及排放数据在国家与工业部门间分布式特性的限制。本文提出了一种新颖的联邦混合预测框架，该框架在保护隐私的联邦学习环境中集成了基于ARIMA的趋势建模、基于GARCH的波动性建模、LSTM-Attention时序表示学习和XGBoost预测。所提出的框架使分布式客户端之间无需交换原始数据即可进行协作学习。在14个客户端上的实验评估显示出强大的预测性能，客户端R²值在0.50至0.97之间，平均为0.73；RMSE值在0.06至2.35之间，平均为1.21；MAPE值在1.5%至11.3%之间，平均为6.5%。结果表明，所提出的框架为协作式碳排放预测提供了一种准确、可扩展且符合法规的解决方案。
英文摘要：
Climate change, primarily driven by carbon dioxide (CO2) emissions, requires accurate forecasting tools to support effective mitigation policies and sustainable development strategies. Existing forecasting approaches typically rely on centralized data collection, which is often restricted by privacy regulations and the distributed nature of emission data across countries and industrial sectors. This paper proposes a novel federated hybrid forecasting framework that integrates ARIMA-based trend modeling, GARCH-based volatility modeling, LSTM-Attention temporal representation learning, and XGBoost prediction within a privacy-preserving federated learning environment. The proposed framework enables collaborative learning among distributed clients without requiring the exchange of raw data. Experimental evaluation across 14 clients demonstrates strong forecasting performance, achieving client R2 values between 0.50 and 0.97 with an average of 0.73, RMSE values ranging from 0.06 to 2.35 with an average of 1.21, and MAPE values between 1.5% and 11.3% with an average of 6.5%. The results indicate that the proposed framework provides an accurate, scalable, and regulation-compliant solution for collaborative carbon-emission forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
