Improving Federated Learning Face Recognition via Privacy-Agnostic ClustersDownload PDF

Published: 28 Jan 2022, Last Modified: 04 May 2025ICLR 2022 SpotlightReaders: Everyone
Abstract: The growing public concerns on data privacy in face recognition can be partly relieved by the federated learning (FL) paradigm. However, conventional FL methods usually perform poorly due to the particularity of the task, \textit{i.e.}, broadcasting class centers among clients is essential for recognition performances but leads to privacy leakage. To resolve the privacy-utility paradox, this work proposes PrivacyFace, a framework largely improves the federated learning face recognition via communicating auxiliary and privacy-agnostic information among clients. PrivacyFace mainly consists of two components: First, a practical Differentially Private Local Clustering (DPLC) mechanism is proposed to distill sanitized clusters from local class centers. Second, a consensus-aware recognition loss subsequently encourages global consensuses among clients, which ergo leads to more discriminative features. The proposed schemes are mathematically proved to be differential private, introduce a lightweight overhead as well as yield prominent performance boosts (\textit{e.g.}, +9.63\% and +10.26\% for TAR@FAR=1e-4 on IJB-B and IJB-C respectively). Extensive experiments and ablation studies on a large-scale dataset have demonstrated the efficacy and practicability of our method.
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/improving-federated-learning-face-recognition/code)
22 Replies

Loading

OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview