FedKLPR: Personalized Federated Learning for Person Re-Identification with Adaptive Pruning

KL-Divergence Regularization Loss (KLL): We introduce a regularization loss function based on KL-divergence that explicitly measures and minimizes the probabilistic divergence between local and personalized model distributions. This theoretically grounded approach effectively prevents model drift while preserving the statistical characteristics of distributed client data, overcoming the limitations of conventional cosine similarity metrics. KL-Divergence-Prune Weighted Aggregation (KLPW A): We introduce a novel aggregation strategy that integrates KL-divergence-based distributional similarity, KL-Divergence-aggregation Weight (KLA W), and client-specific pruning ratios, Pruning-ratio-aggregation Weight (PRA W), into a unified weighting mechanism. This approach dynamically prioritizes clients that exhibit stronger alignment with the global model while contributing compact, efficiently pruned models. By jointly considering statistical consistency and model sparsity, KLPW A surpasses traditional aggregation methods in handling non-IID data distributions and substantially reduces communication costs. Sparse Activation Skipping (SAS): We present a mechanism of skipping pruned parameters during aggregation to enable the global model to be updated only with essential information. Cross-Round Recovery (CRR): To mitigate severe accuracy degradation caused by model pruning, we introduce the CRR, a two-stage pruning strategy. The CRR enables more precise decisions on whether to perform pruning, thus maintaining model accuracy after pruning. The remainder of this paper is organized as follows: Section II introduces related works and background of unsupervised federated person ReID.