3 papers
cs.CL2025
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning
Tianyang Xu, Xiaoze Liu, Feijie Wu +2
Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability t…
cs.CL2025
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…
cs.DC2024
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…