HZ
Hangyu Zhu
Authored
4 records found
PIVODL
Privacy-Preserving Vertical Federated Learning Over Distributed Labels
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been
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FEVERLESS
Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation
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FEVERLESS
Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation
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Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsui
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