Federated Learning for Face Recognition
Jaehyeok Kim, Taehyeong Park, Hyorin Kim, Suhyun Kim · 2021
With the rapid development of deep learning, the accuracy of face recognition has significantly increased. However, training a face recognition model requires the collection of private data to a centralized server to obtain high performance in the desired domain. Since federated learning is a technique to train a model without collecting data to a server, it is a suitable architecture to train a face recognition model with privacy-sensitive face images held in personal smartphones. This study proposes strategies to apply federated learning to face recognition model training.