1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning
Jun Nie, Danyang Xiao, Lei Yang +1
Data heterogeneity across clients is one of the key challenges in Federated Learning (FL), which may slow down the global model convergence and even weaken global model performance…
cs.CR2022
CFL: Cluster Federated Learning in Large-scale Peer-to-Peer Networks
Qian Chen, Zilong Wang, Yilin Zhou +3
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' local devices. However, the parameter server setting of FL not only has high bandw…
cs.LG2020★ 1 cited
PFL-MoE: Personalized Federated Learning Based on Mixture of Experts
Binbin Guo, Yuan Mei, Danyang Xiao +3
Federated learning (FL) is an emerging distributed machine learning paradigm that avoids data sharing among training nodes so as to protect data privacy. Under coordination of the…