19 citations · 48 across the 22 of their papers we have counts for
5 papers · 1 filter
Split Unlearning
Guangsheng Yu, Yanna Jiang, Qin Wang +5
We introduce Split Unlearning, a novel machine unlearning technology designed for Split Learning (SL), enabling the first-ever implementation of Sharded, Isolated, Sliced, and Aggr…
A Secure Aggregation for Federated Learning on Long-Tailed Data
Yanna Jiang, Baihe Ma, Xu Wang +4
As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes. I…
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
Yanna Jiang, Baihe Ma, Xu Wang +5
The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comp…
Location Privacy Threats and Protections in 6G Vehicular Networks: A Comprehensive Review
Baihe Ma, Xu Wang, Xiaojie Lin +8
Location privacy is critical in vehicular networks, where drivers' trajectories and personal information can be exposed, allowing adversaries to launch data and physical attacks th…
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Guangsheng Yu, Xu Wang, Caijun Sun +5
Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the ov…