Vertical federated learning with secure aggregation

Abstract

The method provides for analyzing input and output connections of layers of a received neural network model configured for vertical federated learning. An undirected graph of nodes is generated in which a node having two or more child nodes includes an aggregation operation, based on the analysis of the model in which a model output corresponds to a node of the graph. A layer of the model is identified in which a sum of lower layer outputs are computed. The identified model layer is partitioned into a first part applied respectively to the multiple entities and a second part applied as an aggregator of the output of the first part. The aggregation operation is performed between pairs of lower layer outputs, and multiple forward and backward passes of the neural network model are performed that include secure aggregation and maintain model partitioning in forward and backward passes.