Abstract:Federated Learning (FL) is a decentralized learning paradigm in which multiple clients collaboratively train deep learning models without centralizing their local data and hence preserve data privacy. Real-world applications usually involve a distribution shift across the datasets of the different clients, which hurts the generalization ability of the clients to unseen samples from their respective data distributions. In this work, we address the recently proposed feature shift problem where the clients have d… Show more
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