4 papers · 1 filter
CooperLLM: Cloud-Edge-End Cooperative Federated Fine-tuning for LLMs via ZOO-based Gradient Correction
He Sun, Jinrui Zhou, Li Li +1
Large Language Models (LLMs) perform well on many NLP tasks, but fine-tuning them on resource-constrained mobile devices is challenging due to high memory and computation costs, de…
Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages
Yebo Wu, Jingguang Li, Zhijiang Guo +1
Federated fine-tuning enables Large Language Models (LLMs) to adapt to downstream tasks while preserving data privacy, but its resource-intensive nature limits deployment on edge d…
DiLoCoX: A Low-Communication Large-Scale Training Framework for Decentralized Cluster
Ji Qi, WenPeng Zhu, Li Li +6
The distributed training of foundation models, particularly large language models (LLMs), demands a high level of communication. Consequently, it is highly dependent on a centraliz…
RAP: Runtime Adaptive Pruning for LLM Inference
Huanrong Liu, Chunlin Tian, Xuyang Wei +2
Large language models (LLMs) excel at language understanding and generation, but their enormous computational and memory requirements hinder deployment. Compression offers a potent…