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This paper introduces FedEPD, a framework for federated graph learning under long-tailed data distributions. It uses an energy-guided dual decoupling approach to separate topological purification from semantic recalibration, achieving state-of-the-art performance on benchmarks with up to 4.97% accuracy improvement.