Federated Unsupervised Cluster-Contrastive learning for person Re-identification: A coarse-to-fine approach

Abstract

Person Re-identification (ReID) has attracted considerable interests in recent years, largely driven by the escalating demand for public safety measures. However, the acquisition and handling of sensitive personal data can trigger significant privacy concerns. Federated learning has been introduced as a potential solution to this problem, with the goal of limiting the exposure of sensitive data across different participating entities (clients). Existing methods often depend on labour-intensive data annotations and face difficulties in maintaining cross-domain uniformity. To tackle these challenges, we propose a Federated Unsupervised Cluster-Contrastive (FedUCC) method based on deep learning for Person ReID that follows a generic-to-specific learning strategy. First, FedUCC procures generic knowledge from a conventional federated learning scheme to aggregate and distribute parameters across local clients. Second, specialized knowledge is explored to facilitate client personalization by disentangling client-specific knowledge from generic knowledge through parameter localization. Third, to further enhance effective fine-grained patterns instead of overfitting on specialized client knowledge, we investigate two key aspects: patch-level feature alignment and camera-invariant learning. Comprehensive experiments on eight public benchmark datasets demonstrate the state-of-the-art performance of our proposed method.