Current state-of-the-art deep learning based face recognition (FR) models require a large number of face identities for central training. However, due to the growing privacy awareness, it is prohibited to access the face images on user devices to continually improve face recognition models. Federated Learning (FL) is a technique to address the privacy issue, which can collaboratively optimize the model without sharing the data between clients. In this work, we propose a FL based framework called FedFR to improve the generic face representation in a privacy-aware manner. Besides, the framework jointly optimizes personalized models for the corresponding clients via the proposed Decoupled Feature Customization module. The client-specific personalized model can serve the need of optimized face recognition experience for registered identities at the local device. To the best of our knowledge, we are the first to explore the personalized face recognition in FL setup. The proposed framework is validated to be superior to previous approaches on several generic and personalized face recognition benchmarks with diverse FL scenarios. The source codes and our proposed personalized FR benchmark under FL setup are available at https://github.com/jackie840129/FedFR.

本论文提出了一种基于联邦学习的面部识别框架，名为FedFR，它通过提出的解耦特征自定义模块共同优化对应客户的个性化模型，从而在保护隐私的情况下提高了通用面部表征的质量，同时最大限度地满足了本地设备已注册身份的优化面部识别体验的需求，并且在多个联邦学习场景下进行的实验结果表明，该框架优于以往的方法。

FedFR: 通用与个性化人脸识别联合优化联邦学习框架