Abstract
Person re-identification (Re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without centralized data collection. However, applying FL to real-world re-ID systems faces two major challenges: statistical heterogeneity across clients due to non-IID data distributions, and substantial communication overhead caused by frequent transmission of large-scale models. To address these issues, we propose FedKLPR, a lightweight and communication-efficient federated learning framework for person re-identification. FedKLPR introduces four key components. First, the KL-Divergence Regularization Loss (KLL) constrains local models by minimizing the divergence from the global feature distribution, effectively mitigating the effects of statistical heterogeneity and improving convergence stability under non-IID conditions. Secondly, KL-Divergence-Prune Weighted Aggregation (KLPWA) integrates pruning ratio and distributional similarity into the aggregation process, thereby improving the robustness of the global model while significantly reducing communication overhead. Furthermore, sparse Activation Skipping (SAS) mitigates the dilution of critical parameters during the aggregation of pruned client models by excluding zero-valued weights from the update process. Finally, Cross-Round Recovery (CRR) introduces a dynamic pruning control mechanism that halts pruning when necessary, enabling deeper compression while maintaining model accuracy. Experimental results on eight benchmark datasets demonstrate that FedKLPR achieves significant communication reduction. Compared with the state-of-the-art, FedKLPR reduces 33\%-38\% communication cost on ResNet-50 and 20\%-40\% communication cost on ResNet-34, while maintaining model accuracy within 1\% degradation.
Abstract (translated)
人员重新识别（Re-ID）是智能监控和公共安全中的一个基本任务。联邦学习（FL）通过允许在不进行集中式数据收集的情况下协作模型训练，提供了一种保护隐私的解决方案。然而，在实际的Re-ID系统中应用FL面临着两个主要挑战：由于非独立同分布（non-IID）的数据分布导致客户端之间的统计异质性以及由大规模模型频繁传输引起的大量通信开销。 为了解决这些问题，我们提出了FedKLPR，这是一种轻量级且通信效率高的联邦学习框架，用于人员重新识别。FedKLPR引入了四个关键组件： 1. **KL散度正则化损失（KLL）**：通过最小化从全局特征分布的发散来约束本地模型，有效地减轻统计异质性的影响，并在非独立同分布条件下提高收敛稳定性。 2. **KL散度剪枝加权聚合（KLPWA）**：将剪枝比率和分布相似性集成到聚合过程中，从而提高了全局模型的鲁棒性，并显著减少了通信开销。 3. **稀疏激活跳过（SAS）**：通过在更新过程中排除零值权重，减轻了在合并经过修剪的客户端模型时关键参数被稀释的问题。 4. **跨轮次恢复（CRR）**：引入了一种动态剪枝控制机制，在必要时停止剪枝操作，使更深层次的压缩成为可能，同时保持模型准确性。 实验结果表明，在八个基准数据集上，FedKLPR实现了显著的通信量减少。与现有最佳方法相比，FedKLPR在ResNet-50上的通信成本降低了33%-38%，在ResNet-34上则减少了20%-40%的同时，保持了模型精度不超过1%的退化。
URL
https://arxiv.org/abs/2508.17431
