1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Leaner Training, Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA
Fei Wang, Baochun Li
Memorization in large language models (LLMs) makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explor…
cs.LG2025
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
Fei Wang, Baochun Li
Recent work has shown that gradient updates in federated learning (FL) can unintentionally reveal sensitive information about a client's local data. This risk becomes significantly…
cs.LG2024★ 1 cited
FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge
Tianhang Zheng, Yanlu Li, Bohan Deng +1
Federated learning has emerged as a decentralized approach for training high-performance models without accessing user data. Despite its effectiveness, it is vulnerable to poisonin…