2 papers
cs.LG2025
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data
Jun Liu, Yunming Liao, Hongli Xu +1
Fine-tuning large language models (LLMs) via federated learning, i.e., FedLLM, has been proposed to adapt LLMs for various downstream applications in a privacy-preserving way. To r…
cs.DC2024
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
Jun Liu, Yunming Liao, Hongli Xu +3
Federated fine-tuning (FedFT) has been proposed to fine-tune the pre-trained language models in a distributed manner. However, there are two critical challenges for efficient FedFT…