Pose-guided Enriched Feature Learning for Federated-by-camera Person Re-identification

Extending person re-identification (ReID) to a federated scenario has recently drawn attention due to privacy concerns of individuals, but existing methods mostly assume sufficient diversity in pose variations even within a decentralized client. We focus on a more realistic federated-by-camera scenario, where each client corresponds to a single camera and thus captures only a sparse set of poses. To enrich pose variety, we propose Pose-guided Enriched Feature Learning (PEFL) that explicitly augments pose-diverse samples in the federated ReID scenario. Specifically, a Pose-Extraction Module (PEM) disentangles pose-relevant and pose-irrelevant feature components, where Pose-Relationship Knowledge Distillation (PKD) method helps identify the correct pose and Semantic Consistency Maintenance (SCM) method preserves semantics even with pose changes. In addition, a Compatibility Regularization method ensures the PEM to be compatible with the feature space of the global model. By recombining pose-relevant and -irrelevant components across identities via PEM, our PEFL synthesizes pose-swapped features, thereby largely facilitating contrastive learning of ReID models. Extensive experiments on Market1501 and MSMT17 under the federated-by-camera setting demonstrate that PEFL consistently outperforms federated ReID baselines and their conjunctions with the existing feature augmentation methods; thus achieving state-of-the-art federated ReID performance.