3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.DC2024★ 1 cited
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
Feijie Wu, Xingchen Wang, Yaqing Wang +3
In federated learning (FL), accommodating clients' varied computational capacities poses a challenge, often limiting the participation of those with constrained resources in global…
cs.LG2023★ 3 cited
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT
Jiaqi Wang, Shenglai Zeng, Zewei Long +3
Federated learning (FL) enables multiple clients to train models collaboratively without sharing local data, which has achieved promising results in different areas, including the…
cs.LG2023
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Tianci Liu, Haoyu Wang, Yaqing Wang +3
Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have…