Federated learning multi-party security calculation method and device

The embodiment of the invention relates to the field of machine learning, in particular to a federated learning multi-party security computing method and a device, and aims to improve the model training efficiency and accuracy on the basis of protecting the data security in a multi-party computing process. The method comprises the steps that adopting a data node to divide all individual objects inthe data node into a plurality of object sets according to classification standards; for each object set, adopting the data node to determine feature data of the object set according to the feature data of all the individual objects in the object set; adopting the data node to send the feature data of the object set to a model node, so that the model node performs sample alignment on all the feature data of the same object set according to the feature data of the object set sent by the plurality of data nodes to obtain sample data of the object set, training the federated learning model according to the sample data o