Abstract: In a method for training a federated learning model, a server obtains a target split mode corresponding to a training node in response to determining that the training node satisfies a preset splitting condition. The server notifies a client to perform, based on the target split mode, node splitting. The server performs a next round of training by taking a left subtree node generated by performing the node splitting as a new training node until an updated training node does not satisfy the preset splitting condition. The server performs a next round of training by taking another non-leaf node of the boosting tree as a new training node. The server stops training and generates a target federated learning model in response to determining that a node dataset of the plurality of boosting trees is empty.