Secure Collaborative Training and Inference for XGBoost
Andrew Law, Chester Leung, Rishabh Poddar, Raluca Ada Popa, Chenyu Shi, Octavian Sima, Chaofan Yu, Xingmeng Zhang, Wenting Zheng
Abstract
In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has the potential to greatly benefit all parties involved, but organizations have also encountered obstacles in sharing sensitive data due to business, regulatory, and liability concerns.
Topics & Concepts
Decision treeComputer scienceTraining (meteorology)InferenceMachine learningTraining setTree (set theory)Artificial intelligenceLiabilityKnowledge managementComputer securityBusinessFinanceMathematicsPhysicsMathematical analysisMeteorologySecurity and Verification in ComputingAdvanced Malware Detection TechniquesNetwork Security and Intrusion Detection