In the field of Person Re-identification (ReID), addressing the demands of practical applications in diverse and uncontrollable unseen domains necessitates a focus on Domain Generalization (DG). However, when tackling DG for human-related tasks, the growing awareness of privacy introduces new challenges. Privacy concerns often prevent the sharing of local datasets for global learning, and this limitation in data sharing can impair the generalization ability. Therefore, it becomes imperative to address domain generalization under the constraint of privacy protection. This paper delves into a novel and challenging domain generalization problem that incorporates privacy concerns. We propose a new generalizable ReID network that integrates decentralized learning from non-shared private training data. To mitigate domain variations among clients, we introduce a dynamic aggregation strategy for learning a domain-invariant server model. This strategy adaptively weights clients, guided by domain-invariance principles. To ensure generalization ability with limited client data, we present a domain compensation network. This network augments fictitious domains in the model design to simulate unseen testing situations. In the process of generating fictitious domains, we integrate diversity to avoid meaningless generations, and we constrain fidelity to preserve discrimination. Extensive experiments demonstrate the effectiveness of our method in enhancing generalization ability and privacy protection. Our approach achieves competitive performance on multiple widely used benchmarks.

中文翻译：

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用于人员重新识别的领域广义联邦学习

在行人重识别（ReID）领域，解决多样化且不可控的未见领域的实际应用需求需要关注领域泛化（DG）。然而，在处理与人类相关的任务的 DG 时，日益增长的隐私意识带来了新的挑战。隐私问题通常会阻止共享本地数据集以进行全球学习，而数据共享的这种限制可能会损害泛化能力。因此，在隐私保护的约束下解决领域泛化问题势在必行。本文深入研究了一个新颖且具有挑战性的领域泛化问题，其中包含隐私问题。我们提出了一种新的通用 ReID 网络，该网络集成了来自非共享私人训练数据的去中心化学习。为了减轻客户端之间的域变化，我们引入了一种动态聚合策略来学习域不变服务器模型。该策略在域不变性原则的指导下自适应地对客户端进行加权。为了确保有限客户数据的泛化能力，我们提出了一个域补偿网络。该网络增强了模型设计中的虚拟域，以模拟未见过的测试情况。在生成虚拟域的过程中，我们整合多样性以避免无意义的生成，并限制忠诚度以保留歧视。大量的实验证明了我们的方法在增强泛化能力和隐私保护方面的有效性。我们的方法在多个广泛使用的基准测试中实现了具有竞争力的性能。