通过跨源协作增强风格，提升联邦域泛化能力
Multi-Source Collaborative Style Augmentation and Domain-Invariant Learning for Federated Domain Generalization
- 跨多个源域协同生成更广风格数据
- 结合特征对齐与类别关系蒸馏，学习域不变表示
- 适合隐私保护下多机构联合模型训练
联邦域泛化旨在从多个去中心化的源域中学习可泛化的模型，以部署在未见的目标域上。现有的风格增强方法要么仅在孤立的源域内探索数据风格，要么在数据去中心化场景下对现有源域间进行风格插值，导致风格空间受限。为此，我们提出一种多源协作风格增强与域不变学习方法（MCSAD），用于联邦域泛化。具体而言，设计多源协作风格增强模块，生成更广泛的风格数据；同时通过同类别跨域特征对齐和不同类别间的关系集成蒸馏，实现原始数据与增强数据间的域不变学习。通过交替执行协作风格增强与域不变学习，模型在未见目标域上表现出良好泛化性能。在多个域泛化数据集上的大量实验表明，该方法显著优于当前最先进的联邦域泛化方法。
原文摘要 · Abstract (English)
Federated domain generalization aims to learn a generalizable model from multiple decentralized source domains for deploying on the unseen target domain. The style augmentation methods have achieved great progress on domain generalization. However, the existing style augmentation methods either explore the data styles within isolated source domain or interpolate the style information across existing source domains under the data decentralization scenario, which leads to limited style space. To address this issue, we propose a Multi-source Collaborative Style Augmentation and Domain-invariant learning method (MCSAD) for federated domain generalization. Specifically, we propose a multi-source collaborative style augmentation module to generate data in the broader style space. Furthermore, we conduct domain-invariant learning between the original data and augmented data by cross-domain feature alignment within the same class and classes relation ensemble distillation between different classes to learn a domain-invariant model. By alternatively conducting collaborative style augmentation and domain-invariant learning, the model can generalize well on unseen target domain. Extensive experiments on multiple domain generalization datasets indicate that our method significantly outperforms the state-of-the-art federated domain generalization methods.
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