2 citations · 3 across the 6 of their papers we have counts for
6 papers
MEASER: Malware embedding attacks on open-source LLMs
Ming Tan, Wei Li, Hu Tao +4
Open-source large language models (LLMs) have demonstrated considerable dominance over proprietary LLMs in resolving neural processing tasks, thanks to the collaborative and sharin…
Machine Unlearning in Forgettability Sequence
Junjie Chen, Qian Chen, Jian Lou +3
Machine unlearning (MU) is becoming a promising paradigm to achieve the "right to be forgotten", where the training trace of any chosen data points could be eliminated, while maint…
PAGE: Equilibrate Personalization and Generalization in Federated Learning
Qian Chen, Zilong Wang, Jiaqi Hu +3
Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under th…
Dap-FL: Federated Learning flourishes by adaptive tuning and secure aggregation
Qian Chen, Zilong Wang, Jiawei Chen +2
Federated learning (FL), an attractive and promising distributed machine learning paradigm, has sparked extensive interest in exploiting tremendous data stored on ubiquitous mobile…
CFL: Cluster Federated Learning in Large-scale Peer-to-Peer Networks
Qian Chen, Zilong Wang, Yilin Zhou +3
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' local devices. However, the parameter server setting of FL not only has high bandw…
PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks
Qian Chen, Zilong Wang, Wenjing Zhang +1
The concept of Federated Learning (FL) has emerged as a convergence of machine learning, information, and communication technology. It is vital to the development of machine learni…