AI Federated Learning for Face Recognition at the Edge
Deep learning based face recognition models require massive amount of centralized data to train the model efficiently. For image classification tasks, the centralized data can be utilized from public database. However, for face recognition it is prohibited to access private data. Due to this privacy concern, face recognition under privacy protocol has been one of the most difficult tasks in the era of computer vision. Federate learning which is a form of machine learning model addresses the issue. It helps to train the model with multiple devices or clients without allowing them share the data. In this work, using federated learning we improved both the personalized and generalized model.