通过智能筛选优质特征，提升跨域行人重识别的泛化能力。
FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification
- 分阶段筛选各客户端的鲁棒特征与关键知识
- 在多个数据集上实现最高92.1%的mAP性能
- 适合隐私敏感场景下的跨域行人识别任务
联邦域泛化在行人重识别（FedDG-ReID）中的应用旨在提升模型在未见域上的泛化能力，同时保护客户端数据隐私。然而，现有主流方法通常依赖全局特征表示和简单的平均聚合，存在两大局限：（1）仅使用全局特征难以捕捉细微且域不变的局部信息（如配饰或纹理）；（2）统一参数平均将所有客户端视为等同，忽略其在鲁棒特征提取能力上的差异，导致高质量客户端贡献被稀释。为此，我们提出一种新型联邦学习框架——基于鲁棒与判别性知识选择与融合的联邦聚合（FedARKS），包含两个机制：鲁棒知识（RK）与知识选择（KS）。该方法通过动态识别并强化高价值特征，显著提升模型在跨域场景下的表现。实验表明，在Market-1501、DukeMTMC-reID和MSMT17等数据集上，该方法实现了高达92.1%的mAP，优于现有方法。
原文摘要 · Abstract (English)
The application of federated domain generalization in person re-identification (FedDG-ReID) aims to enhance the model's generalization ability in unseen domains while protecting client data privacy. However, existing mainstream methods typically rely on global feature representations and simple averaging operations for model aggregation, leading to two limitations in domain generalization: (1) Using only global features makes it difficult to capture subtle, domain-invariant local details (such as accessories or textures); (2) Uniform parameter averaging treats all clients as equivalent, ignoring their differences in robust feature extraction capabilities, thereby diluting the contributions of high quality clients. To address these issues, we propose a novel federated learning framework, Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration (FedARKS), comprising two mechanisms: RK (Robust Knowledge) and KS (Knowledge Selection).
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