Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization

arXiv:2403.15605v1 Announce Type: cross
Abstract: 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 …

abstract arxiv collaborative cs.cv cs.lg domain domains global issue machine machine learning normalization performance privacy regularization shift train type