28 citations · 28 across the 2 of their papers we have counts for
3 papers
cs.LG2026
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
Tian Wen, Zhiqin Yang, Yonggang Zhang +4
Federated learning (FL) suffers from performance degradation due to the inevitable presence of noisy annotations in distributed scenarios. Existing approaches have advanced in dist…
cs.SD2023
EnchantDance: Unveiling the Potential of Music-Driven Dance Movement
Bo Han, Teng Zhang, Zeyu Ling +1
The task of music-driven dance generation involves creating coherent dance movements that correspond to the given music. While existing methods can produce physically plausible dan…
cs.LG2023★ 28 cited
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Yu Zheng +4
Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data…