This paper presents a privacy-preserving Multi-Center Federated Learning (MCFL) framework for district heating demand forecasting with a 24-hour prediction horizon. To evaluate the effectiveness of this framework, we conducted a comparative analysis across three models: a monolithic model, a traditional federated learning (FL) model, and the proposed MCFL model. Our results demonstrate that the MCFL model improves the prediction accuracy of the standard FL model by 13.86%, suggesting it as a promising enhancement in federated settings. Furthermore, MCFL is particularly well-suited for district heating forecasting, as it handles data heterogeneity, reinforces privacy protections, and supports scalability, making it an ideal choice for complex, distributed environments.

中文翻译：

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一种用于区域供热预测的隐私保护多中心联邦学习框架


本文提出了一个具有 24 小时预测范围的区域供热需求预测的隐私保护多中心联邦学习 （MCFL） 框架。为了评估该框架的有效性，我们对三个模型进行了比较分析：整体模型、传统联邦学习 （FL） 模型和拟议的 MCFL 模型。我们的结果表明，MCFL 模型将标准 FL 模型的预测准确性提高了 13.86%，表明它在联合环境中是一个有前途的增强功能。此外，MCFL 特别适合区域供热预测，因为它可以处理数据异构性、加强隐私保护并支持可扩展性，使其成为复杂分布式环境的理想选择。