4 papers
Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity
Fei Wu, Jia Hu, Geyong Min +1
Federated low-rank adaptation (FedLoRA) has facilitated communication-efficient and privacy-preserving fine-tuning of foundation models for downstream tasks. In practical federated…
NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty
Xu Zheng, Feiyu Wu, Zhuocheng Wang +2
Language data are increasingly acquired and governed as assets, yet platforms often price candidate resources before knowing their true privacy or access costs. We study online pri…
Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation
Fei Wu, Jia Hu, Geyong Min +1
Driven by the rapid growth of model parameters, parameter-efficient fine-tuning (PEFT) has become essential for adapting large models to diverse downstream tasks under constrained…
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models
Fei Wu, Jia Hu, Geyong Min +1
Pre-trained Language Models (PLMs) have demonstrated their superiority and versatility in modern Natural Language Processing (NLP), effectively adapting to various downstream tasks…