On Defensive Neural Networks against Inference Attack in Federated Learning

Hongkyu Lee, Jeehyeong Kim, Rasheed Hussain, Sunghyun Cho, Junggab Son

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)

Abstract

Federated Learning (FL) is a promising technique for edge computing environments as it provides better data privacy protection. It enables each edge node in the system to send a central server a computed value, named gradient, rather than sending raw data. However, recent research results show that the FL is still vulnerable to an inference attack, which is an adversarial algorithm that is capable of identifying the data used to compute the gradient. One prevalent mitigation strategy is differential privacy which computes a gradient with noised data, but this causes another problem that is accuracy degradation. To effectively deal with this problem, this paper proposes a new digestive neural network (DNN) and integrates it into FL. The proposed scheme distorts raw data by DNN to make it unrecognizable then computes a gradient by a classification network. The gradients generated by edge nodes will be sent to the server to complete a trained model. The simulation results show that the proposed scheme has 9.31% higher classification accuracy and 19.25% lower attack accuracy on average than the differential private schemes.

Original languageEnglish
Title of host publicationICC 2021 - IEEE International Conference on Communications, Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781728171227
DOIs
Publication statusPublished - Jun 2021
Event2021 IEEE International Conference on Communications, ICC 2021 - Virtual, Online, Canada
Duration: 14 Jun 202123 Jun 2021

Publication series

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

Conference

Conference2021 IEEE International Conference on Communications, ICC 2021
Country/TerritoryCanada
CityVirtual, Online
Period14/06/2123/06/21

Bibliographical note

Funding Information:
This work was supported by the MSIT (Ministry of Science, ICT), Korea, under the High-Potential Individuals Global Training Program (2019-0-01601) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). Corresponding Author: Junggab Son (Email: json@kennesaw.edu)

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Deep Learning
  • Differential Privacy
  • Edge Computing
  • Federated Learning
  • Inference Attack

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