通过知识蒸馏与剪枝感知机制，提升跨设备行人重识别的通信效率与稳定性。
FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
- 用KL散度引导训练和参数重要性加权，缓解非独立同分布数据带来的偏差。
- 结合无结构剪枝与动态恢复策略，通信开销降低40%-42%且精度不降。
- 适合资源受限场景下的隐私保护型行人识别系统部署。
行人重识别（re-ID）是智能监控与公共安全中的基础任务。联邦学习（FL）为无需集中收集数据即可协作训练模型提供了隐私保护范式。然而，由于客户端数据存在非独立同分布（non-IID）问题，以及大规模模型频繁传输带来的高通信开销，真实场景中部署FL仍具挑战。为此，本文提出FedKLPR——一种轻量级、通信高效的行人重识别联邦学习框架。该框架包含三个核心组件：首先，引入基于KL散度的训练机制，包括KL散度正则化损失（KLL）和KL散度聚合权重（KLAW），以缓解统计异构性并提升非IID环境下的收敛稳定性；其次，采用无结构剪枝降低通信开销，并提出剪枝比率聚合权重（PRAW）以衡量剪枝后客户端参数的重要性；结合KLAW，形成KL散度-剪枝加权聚合（KLPWA），实现异构数据下剪枝模型的有效聚合；第三，设计跨轮次恢复机制（CRR），自适应控制每轮剪枝强度，防止过度压缩导致精度下降。在八个基准数据集上的实验表明，FedKLPR在保持竞争力性能的同时实现了显著通信节省。相较于现有最优方法，其在ResNet-50上通信成本降低40\\-42\%，整体表现更优。
原文摘要 · Abstract (English)
Person re-identification (re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) provides a privacy-preserving paradigm for collaborative model training without centralized data collection. However, deploying FL in real-world re-ID systems remains challenging due to statistical heterogeneity caused by non-IID client data and the substantial communication overhead incurred by 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 KL-Divergence-aggregation Weight (KLAW), is introduced to mitigate statistical heterogeneity and improve convergence stability under non-IID settings. Second, unstructured pruning is incorporated to reduce communication overhead, and the Pruning-ratio-aggregation Weight (PRAW) is proposed to measure the relative importance of client parameters after pruning. Together with KLAW, PRAW forms KL-Divergence-Prune Weighted Aggregation (KLPWA), enabling effective aggregation of pruned local models under heterogeneous data distributions. Third, Cross-Round Recovery (CRR) adaptively controls pruning across communication rounds to prevent excessive compression and preserve model accuracy. Experiments 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 better overall performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。