VMware, Inc.
DISTRIBUTED AND FEDERATED LEARNING USING MULTI-LAYER MACHINE LEARNING MODELS

Abstract:

In one set of embodiments, a computing node in a plurality of computing nodes can train a first ML model on a local training dataset comprising a plurality of labeled training data instances, where the training is performed using a distributed/federated training approach across the plurality of computing nodes and where the training results in a trained version of the first ML model. The computing node can further compute, using the trained version of the first ML model, a training value measure for each labeled training data instance in the local training dataset and identify a subset of the plurality of labeled training data instances based on the computed training value measures. The computing node can then train a second ML model on the subset, where the training of the second ML model is performed using the distributed/federated training approach.