3 papers
cs.LG2025
Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models
Yongxin Guo, Zhenglin Cheng, Xiaoying Tang +2
The Sparse Mixture of Experts (SMoE) has been widely employed to enhance the efficiency of training and inference for Transformer-based foundational models, yielding promising resu…
cs.LG2025
Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
Yongxin Guo, Xiaoying Tang, Tao Lin
Federated Learning (FL) is an evolving distributed machine learning approach that safeguards client privacy by keeping data on edge devices. However, the variation in data among cl…
cs.LG2024
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing
Yongxin Guo, Lin Wang, Xiaoying Tang +1
Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm. Nonetheless, the substantial distribution shifts among clients pose a considerable challenge…