With the growing attention on data privacy and communication security in face
recognition applications, federated learning has been introduced to learn a
face recognition model with decentralized datasets in a privacy-preserving
manner. However, existing works still face challenges such as unsatisfying
performance and additional communication costs, limiting their applicability in
real-world scenarios. In this paper, we propose a simple yet effective
federated face recognition framework called AdaFedFR, by devising an adaptive
inter-class representation learning algorithm to enhance the generalization of
the generic face model and the efficiency of federated training under strict
privacy-preservation. In particular, our work delicately utilizes feature
representations of public identities as learnable negative knowledge to
optimize the local objective within the feature space, which further encourages
the local model to learn powerful representations and optimize personalized
models for clients. Experimental results demonstrate that our method
outperforms previous approaches on several prevalent face recognition
benchmarks within less than 3 communication rounds, which shows
communication-friendly and great efficiency.

提出了一种简单但有效的联邦人脸识别框架 AdaFedFR，通过设计一种自适应的类间表示学习算法，在严格保护隐私的情况下提高泛化性能和联邦训练的效率。实验结果表明，该方法在几个常见的人脸识别基准上优于以前的方法，仅用不到 3 次通信轮次，展现出友好的通信性能和高效性。