International Business Machines Corporation
FEDERATED ENSEMBLE LEARNING FROM DECENTRALIZED DATA WITH INCREMENTAL AND DECREMENTAL UPDATES

Abstract:

A computer implemented method includes distributing a plurality of prediction models, where each of a plurality of clients initially includes at least one associated prediction model from the plurality of prediction models, among all of the plurality of clients to provide each of the plurality of clients with each of the plurality of prediction models. The plurality of prediction models is evaluated on at least a portion of a local dataset resident on each of the plurality of clients to output a quantification indicating how each of the prediction models fit at least the portion of the local dataset of each of the plurality of clients. An ensemble model is generated by applying weights to each of the plurality of prediction models based on a value, a gradient, and a Hessian matrix of a user-defined objective.