Towards Accurate and Stronger Local Differential Privacy for Federated Learning with Staircase Randomized Response

Author:

Varun Matta1ORCID,Feng Shuya2ORCID,Wang Han3ORCID,Sural Shamik1ORCID,Hong Yuan2ORCID

Affiliation:

1. Indian Institute of Technology Kharagpur, Kharagpur, India

2. University of Connecticut, Storrs, Connecticut, USA

3. University of Kansas, Lawrence, Kansas, USA

Funder

National Science Foundation

Publisher

ACM

Reference68 articles.

1. Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 (2018).

2. Towards federated learning at scale: System design;Bonawitz Keith;Proceedings of machine learning and systems,2019

3. Mahawaga Arachchige Pathum Chamikara, Dongxi Liu, Seyit Camtepe, Surya Nepal, Marthie Grobler, Peter Bertok, and Ibrahim Khalil. 2022. Local Differential Privacy for Federated Learning. In ESORICS, Vol. 13554. Springer, 195--216.

4. Cangxiong Chen and Neill DF Campbell. 2021. Understanding training-data leakage from gradients in neural networks for image classification. arXiv preprint arXiv:2111.10178 (2021).

5. Differentially Private Federated Learning with Local Regularization and Sparsification

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