99 citations · 118 across the 14 of their papers we have counts for
8 papers · 1 filter
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off
Huiqiang Chen, Tianqing Zhu, Tao Zhang +2
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair res…
Semi-Supervised Federated Learning for Keyword Spotting
Enmao Diao, Eric W. Tramel, Jie Ding +1
Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly…
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data
Van Sy Mai, Richard J. La, Tao Zhang
Federated Learning (FL) has emerged as a means of distributed learning using local data stored at clients with a coordinating server. Recent studies showed that FL can suffer from…
Self-Aware Personalized Federated Learning
Huili Chen, Jie Ding, Eric Tramel +4
In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives…
Federated Learning Challenges and Opportunities: An Outlook
Jie Ding, Eric Tramel, Anit Kumar Sahu +3
Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce devel…
Correlated Differential Privacy: Feature Selection in Machine Learning
Tao Zhang, Tianqing Zhu, Ping Xiong +3
Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differe…
Fairness Constraints in Semi-supervised Learning
Tao Zhang, Tianqing Zhu, Mengde Han +3
Fairness in machine learning has received considerable attention. However, most studies on fair learning focus on either supervised learning or unsupervised learning. Very few cons…
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks
Ao Ren, Tao Zhang, Yuhao Wang +5
The rapidly growing parameter volume of deep neural networks (DNNs) hinders the artificial intelligence applications on resource constrained devices, such as mobile and wearable de…