237 citations · 358 across the 20 of their papers we have counts for
5 papers · 1 filter
Securing Federated Learning: A Covert Communication-based Approach
Yuan-Ai Xie, Jiawen Kang, Dusit Niyato +4
Federated Learning Networks (FLNs) have been envisaged as a promising paradigm to collaboratively train models among mobile devices without exposing their local privacy data. Due t…
SCNet: A Neural Network for Automated Side-Channel Attack
Guanlin Li, Chang Liu, Han Yu +4
The side-channel attack is an attack method based on the information gained about implementations of computer systems, rather than weaknesses in algorithms. Information about syste…
Threats to Federated Learning: A Survey
Lingjuan Lyu, Han Yu, Qiang Yang
With the emergence of data silos and popular privacy awareness, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Fe…
FedCoin: A Peer-to-Peer Payment System for Federated Learning
Yuan Liu, Shuai Sun, Zhengpeng Ai +3
Federated learning (FL) is an emerging collaborative machine learning method to train models on distributed datasets with privacy concerns. To properly incentivize data owners to c…
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Jun Zhao, Teng Wang, Tao Bai +7
Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying -differentia…