FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification

Person re-identification (re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) provides a privacy-preserving paradigm by enabling collaborative model training without centralized data collection. However, applying FL to real-world re-ID systems remains challenging due to statistical heterogeneity across clients caused by non-IID data distributions and substantial communication overhead resulting from the frequent transmission of large-scale models. To address these challenges, we propose FedKLPR, a lightweight and communication-efficient federated learning framework for person re-ID. FedKLPR consists of three key components. First, KL-Divergence-Guided training, including the KL-Divergence Regularization Loss (KLL) and the KL-Divergence-aggregation Weight (KLAW), is designed to alleviate statistical heterogeneity and improve convergence stability under non-IID settings. Second, an unstructured pruning strategy is incorporated to reduce communication overhead, and the Pruning-ratio-aggregation Weight (PRAW) is introduced to reflect the relative importance of client parameters. Together with KLAW, PRAW forms a novel aggregation method, namely KL-Divergence-Prune Weighted Aggregation (KLPWA), which enables more effective aggregation of pruned local models under non-IID data distributions and enhances global model robustness. Third, Cross-Round Recovery (CRR) employs a dynamic pruning control mechanism to prevent excessive pruning and preserve model accuracy during iterative compression. Experimental results on eight benchmark datasets demonstrate that FedKLPR achieves substantial communication savings while maintaining competitive accuracy. Compared with state-of-the-art methods, FedKLPR reduces communication cost by 40\%--42\% on ResNet-50 while achieving superior overall performance.