NeurIPS 2022

Conditional Moment Alignment for Improved Generalization in Federated Learning

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In this work, we study model heterogeneous Federated Learning (FL) for classification where different clients have different model architectures. Unlike existing works on model heterogeneity, we neither require access to a public dataset nor do we impose constraints on the model architecture of clients and ensure that the clients' models and data are private. We prove a generalization result, that provides fundamental insights into the role of the representations in FL and propose a theoretically grounded algorithm \textbf{Fed}erated \textbf{C}onditional \textbf{M}oment \textbf{A}lignment (\pap) that aligns class conditional distributions of each client in the feature space.