1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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)…
Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs
Taibiao Zhao, Xiaobing Chen, Mingxuan Sun
The adaptation of large language models (LLMs) to time series forecasting poses unique challenges, as time series data is continuous in nature, while LLMs operate on discrete token…
DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game
Xiaobing Chen, Xiangwei Zhou, Songyang Zhang +1
Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive env…