A Comparative Study of Federated Learning Methods for Face Recognition on Non-IID Dataset

Septia Nusa, Yhudha Juwono, Laila Ma'Rufah, Irfan Armawan, Ary Mazharuddin Shiddiqi

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

Abstract

This study focuses on developing facial recognition technology through deep learning. Investigating accurate models requires large and diverse datasets; however, facial data are inherently private and entail significant privacy and consent considerations. To address this issue, this research proposes a Federated Learning (FL)-based facial recognition using Non-IID datasets. We investigate how differential privacy and various facial recognition models impact the performance of FL. The Transformer-Based Face Recognition (TFR) model outperforms others by delivering higher accuracy and lower runtime. In addition, differential privacy introduces a trade-off: as the noise level increases to ensure privacy, the model's accuracy decreases.

Original languageEnglish
Title of host publicationInternational Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350350524
DOIs
Publication statusPublished - 2024
Event2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024 - Virtual, Online, Indonesia
Duration: 22 Feb 202423 Feb 2024

Publication series

NameInternational Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

Conference

Conference2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024
Country/TerritoryIndonesia
CityVirtual, Online
Period22/02/2423/02/24

Keywords

  • Differential Privacy
  • Face Recognition
  • Federated Learning

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