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April 12, 20260 citationsOpen Access

EXFEDL: Explainable Clustered Federated Learning for Multivariate Residential Load Forecasting

INIvonne NuñezESEric SamikwaTBTorsten Braun

Key Points

  • The research aims to enhance the accuracy and interpretability of residential energy load forecasting using a novel federated learning framework.
  • Developed EXFEDL, an explainable clustered federated learning framework.
  • Grouped households based on temporal similarity for model training.
  • Utilized a temporal convolutional network with global attention for learning cluster-specific models.
  • Employed Shapley Additive explanations for interpretability at household and cluster levels.
  • Implemented a stability controller to monitor behavioral drift and trigger adaptive re-clustering.
  • EXFEDL showed improved forecasting accuracy over centralized and standard federated learning methods.
  • The framework provided consistent explanations for the model outputs.
  • Demonstrated adaptability to evolving load patterns, maintaining reliability over time.

Abstract

Accurate residential energy forecasting faces critical challenges regarding privacy, evolving load patterns, and model transparency. We present EXFEDL, an explainable clustered federated learning framework that groups households by temporal similarity and learns cluster-specific models using a temporal convolutional network with global attention. To enhance interpretability, Shapley Additive explanations provide insights at both household and cluster levels. A stability controller monitors behavioral drift and triggers adaptive re-clustering to maintain reliability over time. Experiments on real-world datasets show that EXFEDL improves forecasting accuracy, explanation consistency, and adaptability compared to centralized and standard federated learning approaches.

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Cite This Study

Nuñez et al. (2026) studied this question.

synapsesocial.com/papers/69db383b4fe01fead37c666bhttps://doi.org/10.48620/96662
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Federated Learning for Energy Load Forecasting in Smart Homes2026
  2. 2Federated Domain Separation for Distributed Forecasting of Non-IID Household Loads2024 · 8 citations
  3. 3Client clustering versus personalization in federated residential load forecasting: A personalization-fair benchmark of accuracy and communication overhead2026
  4. 4Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series2024 · 3 citations
  5. 5Addressing Heterogeneity in Federated Load Forecasting via Personalization Layers2024