Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience-Reference-Cited by-同舟云学术

Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience

Published:2024-05-13 Issue: Volume:1 Page:2986-2997
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Zhang Yanjun¹^ORCID,Sun Ruoxi²^ORCID,Shen Liyue³^ORCID,Bai Guangdong³^ORCID,Xue Minhui²^ORCID,Meng Mark Huasong⁴^ORCID,Li Xue³^ORCID,Ko Ryan³^ORCID,Nepal Surya⁵^ORCID

Affiliation:

1. University of Technology Sydney, Sydney, Australia

2. CSIRO's Data61, Marsfield, Australia

3. The University of Queensland, St Lucia, Australia

4. Institute for Infocomm Research, A*STAR, Singapore, Singapore

5. CSIRO's Data61, Sydney, Australia

Funder

Australian Research Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645545

Reference48 articles.

1. Deep Learning with Differential Privacy

2. How Vulnerable Is Vehicular Communication to Physical Layer Jamming Attacks?

3. Alberto Bietti, Chen-Yu Wei, Miroslav Dudik, John Langford, and Steven Wu. 2022. Personalization improves privacy-accuracy tradeoffs in federated learning. In International Conference on Machine Learning. 1945--1962.

4. Sharemind: A Framework for Fast Privacy-Preserving Computations

5. Towards federated learning at scale: System design;Bonawitz Keith;Proceedings of Machine Learning and Systems,2019

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