3 citations · 5 across the 5 of their papers we have counts for
5 papers
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning
Shenghui Li, Edith C. -H. Ngai, Fanghua Ye +1
Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising paradigm for privacy-preserving and efficient adaptation of Pre-trained Language Models (PLMs) in Fed…
Synergizing Foundation Models and Federated Learning: A Survey
Shenghui Li, Fanghua Ye, Meng Fang +4
Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models…
Turn-Level Active Learning for Dialogue State Tracking
Zihan Zhang, Meng Fang, Fanghua Ye +2
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems. However, collecting a large amount of turn-by-turn annotated dialogue data is costly and in…
Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting
Fanghua Ye, Meng Fang, Shenghui Li +1
Query rewriting plays a vital role in enhancing conversational search by transforming context-dependent user queries into standalone forms. Existing approaches primarily leverage h…
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
Shenghui Li, Edith Ngai, Fanghua Ye +3
Federated learning (FL) facilitates distributed training across different IoT and edge devices, safeguarding the privacy of their data. The inherent distributed structure of FL int…