  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.
