69 citations · 181 across the 15 of their papers we have counts for
9 papers · 1 filter
Knowledge-Injected Federated Learning
Zhenan Fan, Zirui Zhou, Jian Pei +4
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold…
Revealing Unfair Models by Mining Interpretable Evidence
Mohit Bajaj, Lingyang Chu, Vittorio Romaniello +5
The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical…
Improving Fairness for Data Valuation in Horizontal Federated Learning
Zhenan Fan, Huang Fang, Zirui Zhou +4
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federat…
Achieving Model Fairness in Vertical Federated Learning
Changxin Liu, Zhenan Fan, Zirui Zhou +4
Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learn…
Training Fair Models in Federated Learning without Data Privacy Infringement
Xin Che, Jingdi Hu, Zirui Zhou +2
Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data…
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
Amin Banitalebi-Dehkordi, Naveen Vedula, Jian Pei +3
In many industry scale applications, large and resource consuming machine learning models reside in powerful cloud servers. At the same time, large amounts of input data are collec…
Data Pricing in Machine Learning Pipelines
Zicun Cong, Xuan Luo, Pei Jian +2
Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, s…
Robust Counterfactual Explanations on Graph Neural Networks
Mohit Bajaj, Lingyang Chu, Zi Yu Xue +4
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuitio…
Personalized Cross-Silo Federated Learning on Non-IID Data
Yutao Huang, Lingyang Chu, Zirui Zhou +4
Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We…