Achieving Secure Federated Learning Assisted by Covert Communication

Anguo Jiang, Huan Zhou, Rui Chen, Victor Leung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Federated learning (FL) is widely utilized in machine learning by exploiting its capabilities of protecting data privacy. However, due to the openness and broadcast characteristics of wireless channels, the parameters of FL is facing serious security risks during the upload process. Considering the protection of covert communication on communication behavior, a secure FL framework with the aid of covert communication is proposed, which realizes the covert transmission of parameters. Specifically, edge server (ES) sends artificial noise to confuse a malicious eavesdropper (Eve) when mobile devices (MDs) transmit information. Thus, we investigate the joint optimization problem of MDs power and jamming power to minimize the total latency in FL for a given covert constraint. Then, a particle swarm optimisation (PSO) algorithm is proposed in order to address the optimization problem. The results indicate that the proposed framework can successfully achieve covert transmission of model update parameters, thereby enhancing the overall security of the system.

Original languageEnglish
Title of host publicationProceedings - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages91-96
Number of pages6
ISBN (Electronic)9798350312270
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023 - Xi�an, China
Duration: 19 Oct 202322 Oct 2023

Publication series

NameProceedings - 2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

Conference

Conference2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023
Country/TerritoryChina
CityXi�an
Period19/10/2322/10/23

Keywords

  • Data privacy
  • covert communication
  • differential privacy
  • federated learning

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