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cs.LG2026
Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference
Zhiyao Xu, Aoxue Liu, Zhanjie Ding +3
Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert communication and routing-ind…
cs.LG2025
FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
Fucheng Guo, Zeyu Luan, Qing Li +2
Federated Learning (FL) has emerged as an essential framework for distributed machine learning, especially with its potential for privacy-preserving data processing. However, exist…
cs.LG2024
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
Zhengxin Zhang, Dan Zhao, Xupeng Miao +4
Finetuning large language models (LLMs) has been empirically effective on a variety of downstream tasks. Existing approaches to finetuning an LLM either focus on parameter-efficien…