Tag
Energy-Guided Flow Matching improves generative image quality by using a moving endpoint and adaptive scheduling, achieving state-of-the-art FID scores with reduced training cost.
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.