高效组装规范化层和正则化以实现联邦域泛化
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
AI总结：
gPerXAN通过个性化规范化和引导正则化实现联邦域泛化的高效组装，提升模型在域偏移下的泛化能力。
摘要：
域偏移是机器学习中一个严峻的问题，导致模型在测试于未见域时性能下降。联邦域泛化（FedDG）试图在隐私保护的前提下，通过协作客户端训练一个全局模型，使其能够很好地泛化到可能有域偏移的未见客户端。然而，大多数现有的FedDG方法要么导致额外的数据泄露隐私风险，要么引起客户端通信和计算上的显著成本，这些在联邦学习范式中都是主要问题。为克服这些挑战，我们引入了一种新的FedDG架构方法，即gPerXAN，其依赖于一个与引导正则化器配合的规范化方案。具体而言，我们精心设计了个性化显式组装规范化，以强制客户端模型选择性地过滤那些偏向本地数据的域特定特征，同时保留这些特征的判别性。然后，我们结合一个简单而有效的正则化器，引导这些模型直接捕捉域不变的表示，这些表示可以被全局模型的分类器利用。在两个基准数据集，即PACS和Office-Home，以及一个现实世界医学数据集Camelyon17上的广泛实验结果表明，我们提出的方法在解决这一特定问题上优于其他现有方法。
英文摘要：
Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause additional privacy risks of data leakage or induce significant costs in client communication and computation, which are major concerns in the Federated Learning paradigm. To circumvent these challenges, here we introduce a novel architectural method for FedDG, namely gPerXAN, which relies on a normalization scheme working with a guiding regularizer. In particular, we carefully design Personalized eXplicitly Assembled Normalization to enforce client models selectively filtering domain-specific features that are biased towards local data while retaining discrimination of those features. Then, we incorporate a simple yet effective regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets, i.e., PACS and Office-Home, and a real-world medical dataset, Camelyon17, indicate that our proposed method outperforms other existing methods in addressing this particular problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
