Federated learning technique for applied machine learning

A method, a computer program product, and a system of training a machine learning model using federated learning with extreme gradient boosting. The method includes computing an epsilon hyperparameter using training dataset sizes from a first party and a second party. The method also includes transmitting a machine learning model and the epsilon hyperparameter to the first party and the second party and receiving a first model update and a second model update from the first party and the second party respectively. The method further includes fusing the first model update and the second model update to produce a global histogram and determining at least one split candidate in a decision tree used by the machine learning model using the global histogram. The method also includes rebuilding the machine learning model by adding the split candidate to a decision tree of the machine learning model.