19 citations · 22 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
Fengling Lv, Xinyi Shang, Yang Zhou +3
Personalized Federated Learning (PFL) aims to acquire customized models for each client without disclosing raw data by leveraging the collective knowledge of distributed clients. H…
cs.LG2023★ 2 cited
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
cs.LG2023★ 19 cited
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Zexi Li, Tao Lin, Xinyi Shang +1
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and p…