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cs.LG2025
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…
cs.LG2025
Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computing
Song Gao, Songyang Zhang, Shusen Jing +4
Recent advancements in large artificial intelligence models (LAMs) are driving significant innovations in mobile edge computing within next-generation wireless networks. However, t…
cs.LG2025
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)…