Personalized Federated Mutual Learning for Unsupervised Camera-aware Person Re-identification

Abstract

Person re-identification (ReID) is essential for enhancing security and tracking in multi-camera surveillance systems. To achieve effective person re-identification (ReID) performance across diverse datasets, the Federated Unsupervised Person Re-identification via Camera-aware Clustering (FedUCA) approach has made strides in utilizing distributed datasets while ensuring data privacy. Nevertheless, its uniform model may not adequately cater to the specific characteristics of each participant’s data, given the diversity in camera perspectives and client-specific data variances, thus obtaining degraded results. To address this issue, we propose an advanced framework, Personalized Federated Dual-Model Learning for Camera-Aware Person Re-Identification (PerFedDual), which introduces knowledge-sharing techniques inherent to mutual learning for FedUCA with the camera-centric clustering process. PerFedDual supports a dual-model training approach that creates a cooperative learning space that improves both the global model and client-specific models by exchanging knowledge both ways. The methodology adopted is precisely adjusted to the distinctive data environment of each client, ensuring the protection of privacy while simultaneously enhancing the accuracy and flexibility of ReID models in diverse camera configurations. The empirical evaluation reveals that PerFedDual outperforms FedUCA and alternative federated learning strategies, highlighting the benefits of our technique that leverage collective intelligence to enhance unsupervised person re-identification.