The application of federated learning in face recognition: A systematic investigation of the existing frameworks

Abstract

This paper presents a thorough examination of the recent progress made in applying federated learning to the field of face recognition. As face recognition technology continues to gain widespread adoption across various sectors, issues related to data privacy and efficiency have taken center stage. In response, federated learning, characterized by its decentralized machine learning approach, has emerged as a promising solution to tackle these pressing concerns. This review categorises the current federated learning frameworks for face recognition into four main purposes: Training Efficiency, Recognition Accuracy, Data Privacy, and Spoof Attack Detection. Each category is explored in-depth, highlighting the principles, structures, applicability, and advantages of the frameworks. The paper also delves into the challenges faced in the integration of federated learning and face recognition, such as high computational overhead, model inconsistency, and data heterogeneity. The review concludes with recommendations for future research directions, emphasising the need for model compression, asynchronous communication strategies, and techniques to address data heterogeneity. The findings underscore the potential and challenges of applying federated learning in face recognition, paving the way for more secure and efficient facial recognition systems.