659 citations · 851 across the 26 of their papers we have counts for
27 papers
MemHunter: Automated and Verifiable Memorization Detection at Dataset-scale in LLMs
Zhenpeng Wu, Jian Lou, Zibin Zheng +1
Large language models (LLMs) have been shown to memorize and reproduce content from their training data, raising significant privacy concerns, especially with web-scale datasets. E…
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
Ziyue Xu, Mingfeng Xu, Tianchi Liao +2
Recently, the success of large models has demonstrated the importance of scaling up model size. This has spurred interest in exploring collaborative training of large-scale models…
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li, Lele Fu, Tong Wang +6
To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic…
Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform
Yihao Li, Yanyi Lai, Tianchi Liao +2
With the development of practical deep learning models like generative AI, their excellent performance has brought huge economic value. For instance, ChatGPT has attracted more tha…
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
Yihao Li, Yanyi Lai, Chuan Chen +1
Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been d…
CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
Yuecheng Li, Lele Fu, Sheng Huang +3
Recently, graph contrastive learning (GCL) has emerged as one of the optimal solutions for node-level and supervised tasks. However, for structure-related and unsupervised tasks su…