AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning
This work addresses privacy and efficiency issues in face recognition for real-world applications, but it is incremental as it builds on existing federated learning approaches.
The paper tackles the challenge of improving performance and reducing communication costs in federated face recognition by proposing AdaFedFR, which uses adaptive inter-class representation learning to enhance model generalization and training efficiency. Experimental results show it outperforms previous methods on benchmarks in under 3 communication rounds.
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.