1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
Boyang Zhang, Xiaobing Chen, Songyang Zhang +4
Mixture-of-Experts (MoE) models enable scalable neural networks through conditional computation, offering enhanced effectiveness and efficiency for next-generation wireless communi…
Learning to Rank with Top- Fairness
Boyang Zhang, Quanqi Hu, Mingxuan Sun +2
Fairness in ranking models is crucial, as disparities in exposure can disproportionately affect protected groups. Most fairness-aware ranking systems focus on ensuring comparable a…
Pruning and Malicious Injection: A Retraining-Free Backdoor Attack on Transformer Models
Taibiao Zhao, Mingxuan Sun, Hao Wang +2
Transformer models have demonstrated exceptional performance and have become indispensable in computer vision (CV) and natural language processing (NLP) tasks. However, recent stud…
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach
Xiaobing Chen, Boyang Zhang, Xiangwei Zhou +4
The integration of Federated Learning (FL) and Mixture-of-Experts (MoE) presents a compelling pathway for training more powerful, large-scale artificial intelligence models (LAMs)…