Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

NVIDIA researchers and collaborators from Duke University introduced a communication-efficient vertical federated learning method that handles limited overlapping samples, presented at CVPR 2023. The approach reduces communication overhead while maintaining model accuracy in scenarios where data features are distributed across parties with few shared samples.

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