CL-FedFR: Curriculum Learning for Federated Face Recognition

Devilliers Caleb Dube, Çiğdem Erdem, Çiğdem Erdem, Ömer Korçak

2024

Abstract

Face recognition (FR) has been significantly enhanced by the advent and continuous improvement of deep learning algorithms and accessibility of large datasets. However, privacy concerns raised by using and distributing face image datasets have emerged as a significant barrier to the deployment of centralized machine learning algorithms. Recently, federated learning (FL) has gained popularity since the private data at edge devices (clients) does not need to be shared to train a model. FL also continues to drive FR research toward decentralization. In this paper, we propose novel data-based and client-based curriculum learning (CL) approaches for federated FR intending to improve the performance of generic and client-specific personalized models. The data-based curriculum utilizes head pose angles as the difficulty measure and feeds the images from “easy” to “difficult” during training, which resembles the way humans learn. Client-based curriculum chooses “easy clients” based on performance during the initial rounds of training and includes more “difficult clients” at later rounds. To the best of our knowledge, this is the first paper to explore CL for FR in a FL setting. We evaluate the proposed algorithm on MS-Celeb-1M and IJB-C datasets and the results show an improved performance when CL is utilized during training.

Paper Citation

in Harvard Style

Caleb Dube D., Erdem Ç. and Korçak Ö. (2024). CL-FedFR: Curriculum Learning for Federated Face Recognition. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 845-852. DOI: 10.5220/0012574000003660

in Bibtex Style

@conference{visapp24,
author={Devilliers Caleb Dube and Çiğdem Erdem and Ömer Korçak},
title={CL-FedFR: Curriculum Learning for Federated Face Recognition},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={845-852},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012574000003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - CL-FedFR: Curriculum Learning for Federated Face Recognition
SN - 978-989-758-679-8
AU - Caleb Dube D.
AU - Erdem Ç.
AU - Korçak Ö.
PY - 2024
SP - 845
EP - 852
DO - 10.5220/0012574000003660
PB - SciTePress