Federated learning-based YOLOv8 for face detection
Ruijia Peng · Applied and Computational Engineering · 2024
Recognizing the paramount importance of face detection in the realm of computer vision, there is an urgent need to address the vital concern of protecting individuals' privacy. Face detection inherently involves the handling of extremely sensitive personal information. To tackle this challenge, this study puts forth a proposal to incorporate Federated Learning into the face detection model. The objective is to maintain data localization and enhance security throughout the experiments by harnessing the decentralized nature of collaborative learning. The experimental procedure for Federated learning in face recognition models encompasses several key steps: device selection, global model initialization, model distribution to devices, local training, local model updates, model aggregation, global model updates, and multiple iterations. This methodology enables the collective training of models by dispersed devices, hence enhancing recognition performance, all the while ensuring the preservation of user data privacy. In addition, it is imperative to integrate Federated learning with YOLOv8 in order to establish a distributed target detection system. This method entails numerous devices engaging in local YOLOv8 model training, hence safeguarding data privacy and minimising data transmission. The empirical findings indicate that the use of joint learning in the face detection model leads to successful identification of the face model. In the future, there will be a consideration of novel federated learning algorithms with the aim of enhancing privacy.