70 citations · 92 across the 2 of their papers we have counts for
6 papers
Multi-Participant Multi-Class Vertical Federated Learning
Siwei Feng, Han Yu
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated lea…
Transfer Learning with Dynamic Distribution Adaptation
Jindong Wang, Yiqiang Chen, Wenjie Feng +3
Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from diff…
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
Teng Wang, Jun Zhao, Han Yu +4
With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dil…
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
Jiawen Kang, Zehui Xiong, Dusit Niyato +3
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedl…
Easy Transfer Learning By Exploiting Intra-domain Structures
Jindong Wang, Yiqiang Chen, Han Yu +2
Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Unfortunately, existing learning-based meth…
Ethically Aligned Opportunistic Scheduling for Productive Laziness
Han Yu, Chunyan Miao, Yongqing Zheng +3
In artificial intelligence (AI) mediated workforce management systems (e.g., crowdsourcing), long-term success depends on workers accomplishing tasks productively and resting well.…