1.2k citations · 5.2k across the 59 of their papers we have counts for
60 papers
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
Yan Kang, Yang Liu, Yuezhou Wu +2
We present a novel privacy-preserving federated adversarial domain adaptation approach () to address an under-studied but practical cross-silo federated domain adap…
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
Yuezhou Wu, Yan Kang, Jiahuan Luo +2
Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstra…
SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary
Jiaan Wang, Zhixu Li, Qiang Yang +4
Sports game summarization aims to generate news articles from live text commentaries. A recent state-of-the-art work, SportsSum, not only constructs a large benchmark dataset, but…
Federated Deep Learning with Bayesian Privacy
Hanlin Gu, Lixin Fan, Bowen Li +3
Federated learning (FL) aims to protect data privacy by cooperatively learning a model without sharing private data among users. For Federated Learning of Deep Neural Network with…
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
Danqing Zhang, Zheng Li, Tianyu Cao +7
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform t…
FedIPR: Ownership Verification for Federated Deep Neural Network Models
Bowen Li, Lixin Fan, Hanlin Gu +2
Federated learning models are collaboratively developed upon valuable training data owned by multiple parties. During the development and deployment of federated models, they are e…