Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Federated learning has attracted growing interest as it preserves the clients' privacy. As a variant of federated learning, federated transfer learning utilizes the knowledge from similar tasks and thus has also been intensively studied. However, due to the limited radio spectrum, the communication efficiency of federated learning via wireless links is critical since some tasks may require thousands of Terabytes of uplink payload. In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches. We first introduce the system design in which the extracted features and outputs are uploaded instead of parameter updates, and then determine the required payload with this approach and provide comparisons with the existing approaches. Subsequently, we analyze the random shuffling scheme that preserves the clients' privacy. Finally, we evaluate the performance of the proposed learning scheme via experiments on an image classification task to show its effectiveness.

Original languageEnglish (US)
Title of host publication2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3875-3880
Number of pages6
ISBN (Electronic)9781665435406
DOIs
StatePublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: Dec 4 2022Dec 8 2022

Publication series

Name2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings

Conference

Conference2022 IEEE Global Communications Conference, GLOBECOM 2022
Country/TerritoryBrazil
CityVirtual, Online
Period12/4/2212/8/22

Keywords

  • Federated learning
  • computer vision
  • transfer learning
  • wireless communication

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality

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