1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.DC2024
The Impact of Cut Layer Selection in Split Federated Learning
Justin Dachille, Chao Huang, Xin Liu
Split Federated Learning (SFL) is a distributed machine learning paradigm that combines federated learning and split learning. In SFL, a neural network is partitioned at a cut laye…
cs.LG2024
FedUV: Uniformity and Variance for Heterogeneous Federated Learning
Ha Min Son, Moon-Hyun Kim, Tai-Myoung Chung +2
Federated learning is a promising framework to train neural networks with widely distributed data. However, performance degrades heavily with heterogeneously distributed data. Rece…
cs.DC2024★ 1 cited
Convergence Analysis of Split Federated Learning on Heterogeneous Data
Pengchao Han, Chao Huang, Geng Tian +2
Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, w…