Explainable Clustered Federated Learning for Solar Energy Forecasting

Syed Saqib Ali, Mazhar Ali, Dost Muhammad Saqib Bhatti and Bong Jun Choi ()
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Syed Saqib Ali: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
Mazhar Ali: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
Dost Muhammad Saqib Bhatti: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
Bong Jun Choi: School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea

Energies, 2025, vol. 18, issue 9, 1-19

Abstract: Explainable Artificial Intelligence (XAI) is a well-established and dynamic field defined by an active research community that has developed numerous effective methods for explaining and interpreting the predictions of advanced machine learning models, including deep neural networks. Clustered Federated Learning (CFL) mitigates the difficulties posed by heterogeneous clients in traditional federated learning by categorizing related clients according to data characteristics, facilitating more tailored model updates, and improving overall learning efficiency. This paper introduces Explainable Clustered Federated Learning (XCFL), which adds explainability to clustered federated learning. Our method improves performance and explainability by selecting features, clustering clients, training local clients, and analyzing contributions using SHAP values. By incorporating feature-level contributions into cluster and global aggregation, XCFL ensures a more transparent and data-driven model update process. Weighted aggregation by feature contributions improves consumer diversity and decision transparency. Our results show that XCFL outperforms FedAvg and other clustering methods. Our feature-based explainability strategy improves model performance and explains how features affect clustering and model adjustments. XCFL’s improved accuracy and explainability make it a promising solution for heterogeneous and distributed learning environments.

Keywords: Explainable Artificial Intelligence; solar power forecasting; Clustered Federated Learning; machine learning; renewable energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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