Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years. An important issue of face recognition is data privacy, which receives more and more public concerns. As a common privacy-preserving technique, Federated Learning is proposed to train a model cooperatively without sharing data between parties. However, as far as we know, it has not been successfully applied in face recognition. This paper proposes a framework named FedFace to innovate federated learning for face recognition. Specifically, FedFace relies on two major innovative algorithms, Partially Federated Momentum (PFM) and Federated Validation (FV). PFM locally applies an estimated equivalent global momentum to approximating the centralized momentum-SGD efficiently. FV repeatedly searches for better federated aggregating weightings via testing the aggregated models on some private validation datasets, which can improve the model's generalization ability. The ablation study and extensive experiments validate the effectiveness of the FedFace method and show that it is comparable to or even better than the centralized baseline in performance.

中文翻译：

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联合人脸识别

近年来，人脸识别已在计算机视觉和人工智能社区中进行了广泛的研究。人脸识别的一个重要问题是数据隐私，它越来越受到公众的关注。作为一种常见的隐私保护技术，提出了联合学习来协作训练模型而无需在各方之间共享数据。然而，据我们所知，它还没有成功地应用于人脸识别。本文提出了一个名为FedFace的框架，以创新用于面部识别的联合学习。具体来说，FedFace依靠两种主要的创新算法，部分联合动量（PFM）和联合验证（FV）。PFM在本地应用估计的等效动量来有效地近似集中式动量SGD。FV通过在一些专用验证数据集上测试聚合模型来反复搜索更好的联合聚合权重，这可以提高模型的泛化能力。消融研究和广泛的实验验证了FedFace方法的有效性，并表明该方法在性能上与集中式基线相当甚至更好。