18 citations · 27 across the 4 of their papers we have counts for
4 papers
FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation
Ruiyang Wang, Rong Pan, Zhengan Yao
Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data. Nevertheless, recent studies show that conventional FL algorit…
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization
Rongfeng Pan, Jianzong Wang, Lingwei Kong +2
Text summarization is essential for information aggregation and demands large amounts of training data. However, concerns about data privacy and security limit data collection and…
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint
Jingwen Zhang, Yuezhou Wu, Rong Pan
Federated learning makes it possible for all parties with data isolation to train the model collaboratively and efficiently while satisfying privacy protection. To obtain a high-qu…
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
Jingwen Zhang, Yuezhou Wu, Rong Pan
Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML. A large number of worke…