DISTRIBUTED MACHINE LEARNING WITH NEW LABELS USING HETEROGENEOUS LABEL DISTRIBUTION

A method for distributed machine learning (ML) which includes providing a first dataset including a first set of labels to a plurality of local computing devices including a first local computing device and a second local computing device. The method further includes receiving, from the first local computing device, a first set of ML model probabilities values from training a first local ML model using the first set of labels. The method further includes receiving, from the second local computing device, a second set of ML model probabilities values from training a second local ML model using the first set of labels and one or more labels different from any label in the first set of labels. The method further includes generating a weights matrix using the received first set of ML model probabilities values and the received second set of MIL model probabilities values. The method further includes generating a third set of ML model probabilities values by sampling using the generated weights matrix.