用联邦学习融合统计与神经网络，实现跨国碳排放精准预测。
Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models
- 联邦框架下联合ARIMA、GARCH、LSTM-Attention与XGBoost模型
- 14个客户端平均R²达0.73，平均MAPE为6.5%
- 适合关注碳中和政策与隐私保护建模的研究者
气候变化主要由二氧化碳（CO2）排放驱动，亟需精准的预测工具以支持有效的减缓政策与可持续发展战略。现有方法多依赖集中式数据收集，但受隐私法规及排放数据分散于各国与产业部门的限制。本文提出一种新型联邦混合预测框架，集成基于ARIMA的趋势建模、基于GARCH的波动性建模、基于LSTM-Attention的时间表示学习以及XGBoost预测，并在隐私保护的联邦学习环境中运行。该框架允许分布式客户端协作学习而无需共享原始数据。在14个客户端上的实验评估显示，模型表现优异，客户端R²值在0.50至0.97之间，平均0.73；RMSE值范围为0.06至2.35，平均1.21；MAPE值介于1.5%至11.3%，平均6.5%。结果表明，该框架提供了准确、可扩展且符合监管要求的协作式碳排放预测方案。
原文摘要 · Abstract (English)
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.
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