4 citations · 10 across the 6 of their papers we have counts for
8 papers
Do Pre-trained Language Models Indeed Understand Software Engineering Tasks?
Yao Li, Tao Zhang, Xiapu Luo +3
Artificial intelligence (AI) for software engineering (SE) tasks has recently achieved promising performance. In this paper, we investigate to what extent the pre-trained language…
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…
Correlated Data in Differential Privacy: Definition and Analysis
Tao Zhang, Tianqing Zhu, Renping Liu +1
Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a d…