Trustworthy AI Using Confidential Federated Learning

Authors

Andrew Paverd (ORCID: https://orcid.org/0000-0003-2188-5285)
Jinnan Guo
Kapil Vaswani
Peter Pietzuch (ORCID: https://orcid.org/0000-0002-6963-5640)

Institutions

Microsoft (United States) (US)
Imperial College London (GB)

Publication Details

Journal: Communications of the ACM
Published: 2024-08-15
DOI: https://doi.org/10.1145/3677390
Citations: 2
Primary Topic: Cryptography and Data Security
Type: article
Field-Weighted Citation Impact: 0.69

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article

Trustworthy AI Using Confidential Federated Learning

Andrew Paverd, Jinnan Guo, Kapil Vaswani, Peter Pietzuch

Aug 15, 2024

2 citations

Communications of the ACM

Cryptography and Data Security

0.69

article

Trustworthy AI Using Confidential Federated Learning

Andrew Paverd, Jinnan Guo, Kapil Vaswani, Peter Pietzuch

August 15, 2024article en

2 citations

Abstract

No abstract available for this paper.

Communications of the ACMVol. 67(9)

Microsoft (United States) (US), Imperial College London (GB)

Openalex Percentile: Top 25%

Cryptography and Data Security

0.69

DOI Open Access PDF OpenAlex

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Trustworthy AI Using Confidential Federated Learning — Andrew Paverd, Jinnan Guo, et al. · Communications of the ACM (2024) | TGRS Research Map | TGRS