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PROV-FL : privacy-preserving round optimal verifiable federated learning
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Dasu, Vishnu Asutosh, Sarkar, Sumanta and Mandal, Kalikinkar (2022) PROV-FL : privacy-preserving round optimal verifiable federated learning. In: The 15th ACM Workshop on Artificial Intelligence and Security (AISec 2022), Los Angeles, U.S.A ; Hybrid, 11 Nov 2022. Published in: AISec'22: Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security pp. 33-44. ISBN 9781450398800. doi:10.1145/3560830.3563729
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WRAP-PROV-FL-privacy-preserving-round-optimal-verifiable-federated-learning-Sarkar-2022.pdf - Accepted Version - Requires a PDF viewer. Download (1496Kb) | Preview |
Official URL: https://doi.org/10.1145/3560830.3563729
Abstract
Federated learning is a distributed framework where a server computes a global model by aggregating the local models trained on users’ private data. However, for a stronger data privacy guarantee, the server should not access the local models except the aggregated one. One way to achieve this is to use a secure aggregation protocol that comes with the cost of several rounds of interactions between the server and users in the absence of a fully trusted third party (TTP). In this paper, we present PROV-FL, an effcient privacy-preserving federated learning training system that securely aggregates users’ local models. PROV-FL requires only one round of communication between the server and users for aggregating local models without a TTP. Based on the homomorphic encryption and differential privacy techniques, we develop two PROV-FL training protocols for two different, namely single and multi-aggregator, scenarios. PROV-FL enjoys the verifiability feature in which the server can verify the authenticity of the aggregated model and effciently handles users’ dynamic joining and leaving. We evaluate and compare the performance of PROV-FL by running experiments on training CNN/DNN models with a diverse set of real-world datasets.
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Federated database systems, Computer security, Artificial intelligence | ||||||
Journal or Publication Title: | AISec'22: Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security | ||||||
Publisher: | ACM | ||||||
ISBN: | 9781450398800 | ||||||
Official Date: | 7 November 2022 | ||||||
Dates: |
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Page Range: | pp. 33-44 | ||||||
DOI: | 10.1145/3560830.3563729 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in AISec'22: Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security, 07 Nov 2022 http://doi.acm.org/10.1145/3560830.3563729 | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 11 October 2022 | ||||||
Date of first compliant Open Access: | 13 December 2022 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | The 15th ACM Workshop on Artificial Intelligence and Security (AISec 2022) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Los Angeles, U.S.A ; Hybrid | ||||||
Date(s) of Event: | 11 Nov 2022 | ||||||
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