3 papers
cs.CR2026
VFEFL: Privacy-Preserving Federated Learning against Malicious Clients via Verifiable Functional Encryption
Nina Cai, Jinguang Han, Weizhi Meng
Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protecting data privacy. Howev…
cs.GT2026
Privacy as Commodity: MFG-RegretNet for Large-Scale Privacy Trading in Federated Learning
Kangkang Sun, Jianhua Li, Xiuzhen Chen +2
Federated Learning (FL) has emerged as a prominent paradigm for privacy-preserving distributed machine learning, yet two fundamental challenges hinder its large-scale adoption. Fir…
cs.CR2025
Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption
Minjie Wang, Jinguang Han, Weizhi Meng
In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage problem still threatens the pr…