Federated Learning for Secure and Privacy-Preserving Facial Recognition: Advances, Challenges, and Research Directions

Federated learning is an innovative, decentralized machine learning paradigm that allows multiple devices or entities to collaboratively train a shared model without transferring data to a central server. By keeping data localized, this distributed approach ensures enhanced privacy and security for each participating node. Facial recognition, a rapidly evolving field, leverages deep learning techniques to achieve remarkable advancements, often surpassing human-level performance on certain datasets. However, the sensitive nature of facial data, which contains personally identifiable information, raises significant privacy and security concerns. Federated learning has emerged as a promising solution to address these privacy challenges in the facial recognition community. This paper presents a comprehensive review of existing literature on facial recognition frameworks utilizing federated learning. The reviewed techniques are systematically categorized to provide a structured analysis, emphasizing their contributions and relevance to the broader domain of federated learning-based facial recognition. Specifically, this work aims to summarize and analyze various federated learning-based facial recognition methods, their underlying techniques, and their objectives. Furthermore, it offers a high-level perspective on how different functionalities and design principles of federated learning have been applied in facial recognition applications. By doing so, this review identifies key challenges and highlights promising research directions for future advancements in the field.