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cs.CR2024★ 1 cited
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…
cs.CR2022
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…