IPFed: Identity protected federated learning for user authentication

arXiv:2405.03955v1 Announce Type: cross
Abstract: With the development of laws and regulations related to privacy preservation, it has become difficult to collect personal data to perform machine learning. In this context, federated learning, which is distributed learning without sharing personal data, has been proposed. In this paper, we focus on federated learning for user authentication. We show that it is difficult to achieve both privacy preservation and high accuracy with existing methods. To address these challenges, we propose IPFed which …

abstract arxiv authentication become context cs.cv cs.lg data development distributed distributed learning federated learning focus identity laws machine machine learning paper personal data preservation privacy regulations type