Grouped aggregation in federated learning

A computer system, the computer system comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
• initializing a plurality of aggregation groups including a plurality of parties and a plurality of local aggregators;
• initializing a plurality of aggregation groups including a plurality of parties and a plurality of local aggregators;
• submitting a query to a first party from the plurality of parties;
• submitting an initial response to the query from the first party or a second party from the plurality of parties to a first local aggregator from the plurality of local aggregators;
• submitting a final response from the first local aggregator or a second local aggregator from the plurality of local aggregators to a global aggregator;
• and
• building a machine learning model based on the final response.

A computer program product, the computer program product comprising:

• one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising:
• initializing a plurality of aggregation groups including a plurality of parties and a plurality of local aggregators;
• initializing a plurality of aggregation groups including a plurality of parties and a plurality of local aggregators;
• submitting a query to a first party from the plurality of parties;
• submitting an initial response to the query from the first party or a second party from the plurality of parties to a first local aggregator from the plurality of local aggregators;
• submitting a final response from the first local aggregator or a second local aggregator from the plurality of local aggregators to a global aggregator;
• and
• building a machine learning model based on the final response.