FedEPD decouples topological purification from semantic recalibration using energy-guided pruning and prototype injection to improve minority performance in federated long-tailed graph learning.
arXiv preprint arXiv:2503.11414 , year=
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CARE is a parameter-efficient framework that aggregates predictions from noisy labels, VLM text embeddings, and visual features with class-frequency-based agreement thresholds to rectify labels in long-tailed noisy datasets.
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Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach
FedEPD decouples topological purification from semantic recalibration using energy-guided pruning and prototype injection to improve minority performance in federated long-tailed graph learning.