Over-the-Air Federated Learning with Enhanced Privacy

Xiaochan Xue, Moh Khalid Hasan, Shucheng Yu, Laxima Niure Kandel, Min Song

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

1 Scopus citations

Abstract

Federated learning (FL) has emerged as a promising learning paradigm in which only local model parameters (gradients) are shared. Private user data never leaves the local devices thus preserving data privacy. However, recent research has shown that even when local data is never shared by a user, exchanging model parameters without protection can also leak private information. Moreover, in wireless systems, the frequent transmission of model parameters can cause tremendous bandwidth consumption and network congestion when the model is large. To address this problem, we propose a new FL framework with efficient over-the-air parameter aggregation and strong privacy protection of both user data and models. We achieve this by introducing pairwise cancellable random artificial noises (PCR-ANs) on end devices. As compared to existing over-the-air computation (AirComp) based FL schemes, our design provides stronger privacy protection. We analytically show the secrecy capacity and the convergence rate of the proposed wireless FL aggregation algorithm.

Original languageEnglish
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
Pages4546-4551
Number of pages6
ISBN (Electronic)9781538674628
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Communications, ICC 2023 - Rome, Italy
Duration: 28 May 20231 Jun 2023

Publication series

NameIEEE International Conference on Communications
Volume2023-May
ISSN (Print)1550-3607

Conference

Conference2023 IEEE International Conference on Communications, ICC 2023
Country/TerritoryItaly
CityRome
Period28/05/231/06/23

Keywords

  • Over-the-air computation (AirComp)
  • federated learning
  • wireless multiple-access channel

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