8 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
On the Vulnerability of Backdoor Defenses for Federated Learning
Pei Fang, Jinghui Chen
Federated Learning (FL) is a popular distributed machine learning paradigm that enables jointly training a global model without sharing clients' data. However, its repetitive serve…
cs.LG2021★ 8 cited
Loss Tolerant Federated Learning
Pengyuan Zhou, Pei Fang, Pan Hui
Federated learning has attracted attention in recent years for collaboratively training data on distributed devices with privacy-preservation. The limited network capacity of mobil…
cs.LG2020★ 3 cited
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
Pei Fang, Zhendong Cai, Hui Chen +1
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine le…