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Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving way.
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Secureboost: A lossless federated learning framework
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A quasi-newton method based vertical federated learning framework for logistic regression
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VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning. In SIGMOD . 563–576
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Achieving Model Fairness in Vertical Federated Learning
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Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. In AAAI . 10896–10904
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