Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach

The paper introduces FedEPD, a novel framework for Federated Graph Learning that addresses the challenges posed by long-tailed data distributions. It employs a dual decoupling approach to separate topological purification from semantic recalibration, utilizing distribution-aware Dirichlet energy pruning and a two-stage alternating optimization strategy. FedEPD achieves state-of-the-art performance, with improvements of up to 4.97% in accuracy and 5.48% in Macro-F1 across various long-tailed benchmarks, making it significant for practitioners dealing with imbalanced data in collaborative environments.