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Abstract Introduction: Accurate load forecasting plays a vital role in optimizing energy distribution and integrating renewable energy sources within smart grid systems. However, traditional centralized deep learning approaches present major challenges related to data privacy, communication overhead, and scalability, particularly in scenarios involving distributed energy consumers.
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