3 citations · 8 across the 10 of their papers we have counts for
5 papers · 1 filter
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
Feijie Wu, Xingchen Wang, Yaqing Wang +3
In federated learning (FL), accommodating clients' varied computational capacities poses a challenge, often limiting the participation of those with constrained resources in global…
Towards Federated RLHF with Aggregated Client Preference for LLMs
Feijie Wu, Xiaoze Liu, Haoyu Wang +3
Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using user preference data, enabling it to generate content aligned with human…
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
Feijie Wu, Zitao Li, Yaliang Li +2
Large language models (LLMs) show amazing performance on many domain-specific tasks after fine-tuning with some appropriate data. However, many domain-specific data are privately d…
SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation
Xiaoze Liu, Ting Sun, Tianyang Xu +4
Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting…
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
Xiaoze Liu, Feijie Wu, Tianyang Xu +4
The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern fo…