237 citations · 445 across the 10 of their papers we have counts for
10 papers
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…
Multi-Participant Multi-Class Vertical Federated Learning
Siwei Feng, Han Yu
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated lea…
FOCUS: Dealing with Label Quality Disparity in Federated Learning
Yiqiang Chen, Xiaodong Yang, Xin Qin +3
Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications. Federated Learning (FL) is highly useful for such applications, due to s…
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Yang Liu, Anbu Huang, Yun Luo +7
Visual object detection is a computer vision-based artificial intelligence (AI) technique which has many practical applications (e.g., fire hazard monitoring). However, due to priv…
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…