Vertical federated learning(VFL)can learn a common machine learning model over vertically partitioned datasets.However,VFL are faced with these thorny problems:(1)both the training and prediction are very vulnerable t...Vertical federated learning(VFL)can learn a common machine learning model over vertically partitioned datasets.However,VFL are faced with these thorny problems:(1)both the training and prediction are very vulnerable to stragglers;(2)most VFL methods can only support a specific machine learning model.Suppose that VFL incorporates the features of centralised learning,then the above issues can be alleviated.With that in mind,this paper proposes a new VFL scheme,called FedBoost,which makes private parties upload the compressed partial order relations to the honest but curious server before training and prediction.The server can build a machine learning model and predict samples on the union of coded data.The theoretical analysis indicates that the absence of any private party will not affect the training and prediction as long as a round of communication is achieved.Our scheme can support canonical tree-based models such as Tree Boosting methods and Random Forests.The experimental results also demonstrate the availability of our scheme.展开更多
基金National Natural Science Foundation of China(Grant Nos.62166004 and U21A20474)uangxi Science and Technology Major Project(Grant No.AA22068070)Key scientific research project of colleges and universities in Henan Province(Grant No.22B520047).
文摘Vertical federated learning(VFL)can learn a common machine learning model over vertically partitioned datasets.However,VFL are faced with these thorny problems:(1)both the training and prediction are very vulnerable to stragglers;(2)most VFL methods can only support a specific machine learning model.Suppose that VFL incorporates the features of centralised learning,then the above issues can be alleviated.With that in mind,this paper proposes a new VFL scheme,called FedBoost,which makes private parties upload the compressed partial order relations to the honest but curious server before training and prediction.The server can build a machine learning model and predict samples on the union of coded data.The theoretical analysis indicates that the absence of any private party will not affect the training and prediction as long as a round of communication is achieved.Our scheme can support canonical tree-based models such as Tree Boosting methods and Random Forests.The experimental results also demonstrate the availability of our scheme.
