3 papers
cs.LG2024
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content
Guangjing Huang, Qiong Wu, Jingyi Li +1
Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data. To alleviate the…
cs.CR2023
Chained-DP: Can We Recycle Privacy Budget?
Jingyi Li, Guangjing Huang, Liekang Zeng +2
Privacy-preserving vector mean estimation is a crucial primitive in federated analytics. Existing practices usually resort to Local Differentiated Privacy (LDP) mechanisms that inj…
cs.AI2022
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective
Guangjing Huang, Xu Chen, Tao Ouyang +3
Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that…