Vertical federated learning with secure aggregation

A computer system for training a neural network model using vertical federated learning, the computer system comprising:

• one or more computer processors;
• at least one computer-readable storage medium;
• program instructions stored on the at least one computer-readable storage medium, the program instructions comprising:
• program instructions to analyze input and output connections of layers of a neural network model that are received, wherein the neural network model is structured to receive input from vertically partitioned data across multiple entities;
• program instructions to generate an undirected graph of nodes in which a node having two or more child nodes includes an aggregation operation, based on an analysis of the neural network model in which an output of a layer of the neural network model corresponds to a node of the graph;
• program instructions to identify the layer of the neural network model in which a sum of lower layer outputs are computed;
• program instructions to partition the identified neural network model layer into a first part applied respectively to the multiple entities and a second part applied to an aggregator;
• program instructions to perform the aggregation operation between at least two of the lower layer outputs;
• and
• program instructions to perform multiple forward and backward passes of the neural network model including application of secure aggregation in the multiple forward and backward passes, respectively.

A computer program product for training a neural network model using vertical federated learning, the computer system comprising:

• at least one computer-readable storage medium;
• and
• program instructions stored on the at least one computer-readable storage medium, the program instructions comprising:
• program instructions to analyze input and output connections of layers of a neural network model that is received, wherein the neural network model is structured to receive input from vertically partitioned data across multiple entities;
• program instructions to generate an undirected graph of nodes in which a node having two or more child nodes includes an aggregation operation, based on an analysis of the neural network model in which an output of a layer of the neural network model corresponds to a node of the graph;
• program instructions to identify the layer of the neural network model in which a sum of lower layer outputs are computed;
• program instructions to partition the identified neural network model layer into a first part applied respectively to the multiple entities and a second part applied to an aggregator;
• program instructions to perform the aggregation operation between at least two of the lower layer outputs;
• and
• program instructions to perform multiple forward and backward passes of the neural network model including application of secure aggregation in the multiple forward and backward passes, respectively.