41 citations · 77 across the 41 of their papers we have counts for
7 papers · 1 filter
CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation
Yanan Ma, Qiyuan Chen, Zihan Fang +2
Federated low-rank adaptation (LoRA) enables collaborative fine-tuning of large language models without centralizing private client data. Its factorized update, however, creates a…
Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers
Senkang Hu, Yong Dai, Xudong Han +4
Long-horizon LLM agents depend on intermediate information-gathering turns, yet training feedback is usually observed only at the final answer, because process-level rewards requir…
DSP-Reg: Domain-Sensitive Parameter Regularization for Robust Domain Generalization
Xudong Han, Senkang Hu, Yihang Tao +4
Domain Generalization (DG) is a critical area that focuses on developing models capable of performing well on data from unseen distributions, which is essential for real-world appl…
HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
Zihan Fang, Zheng Lin, Senkang Hu +5
While federated learning (FL) enables fine-tuning of large language models (LLMs) without compromising data privacy, the substantial size of an LLM renders on-device training impra…
Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
Senkang Hu, Yanan Ma, Yihang Tao +5
Large language models (LLMs) have achieved remarkable success in various tasks, such as decision-making, reasoning, and question answering. They have been widely used in edge devic…
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
Zihan Fang, Zheng Lin, Zhe Chen +3
Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). However, for many downstream tasks,…
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Guangyu Zhu, Yiqin Deng, Xianhao Chen +3
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the res…