Journal article
Heterogeneous Differential-Private Federated Learning: Trading Privacy for Utility Truthfully
Abstract
Differential-private federated learning (DP-FL) has emerged to prevent privacy leakage when disclosing encoded sensitive information in model parameters. However, the existing DP-FL frameworks usually preserve privacy homogeneously across clients, while ignoring the different privacy attitudes and expectations. Meanwhile, DP-FL is hard to guarantee that uncontrollable clients (i.e., stragglers) have truthfully added the expected DP noise. To …
Authors
Lin X; Wu J; Li J; Sang C; Hu S; Deen MJ
Journal
IEEE Transactions on Dependable and Secure Computing, Vol. 20, No. 6, pp. 5113–5129
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
10.1109/tdsc.2023.3241057
ISSN
1545-5971