2 papers
cs.CL2025
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs
Zige Wang, Qi Zhu, Fei Mi +3
Gradient-based data influence approximation has been leveraged to select useful data samples in the supervised fine-tuning of large language models. However, the computation of gra…
cs.LG2024
Upcycling Noise for Federated Unlearning
Jianan Chen, Qin Hu, Fangtian Zhong +2
In Federated Learning (FL), multiple clients collaboratively train a model without sharing raw data. This paradigm can be further enhanced by Differential Privacy (DP) to protect l…