7 citations · 24 across the 28 of their papers we have counts for
11 papers
FedMCSA: Personalized Federated Learning via Model Components Self-Attention
Qi Guo, Yong Qi, Saiyu Qi +2
Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data. However, Non-IID data of clients presents a tough…
FedComm: Understanding Communication Protocols for Edge-based Federated Learning
Gary Cleland, Di Wu, Rehmat Ullah +1
Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exc…
An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors
Yuanyi Liu, Jia Chen, Di Wu
Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high…
An Online Sparse Streaming Feature Selection Algorithm
Feilong Chen, Di Wu, Jie Yang +1
Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applica…
DPAUC: Differentially Private AUC Computation in Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on…
Differentially Private AUC Computation in Vertical Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federa…