Abstract: A system and a method for training non-parametric Machine Learning (ML) model instances in a collaborative manner is disclosed. A non-parametric ML model instance is trained at each of a plurality of data processing nodes to obtain a plurality of non-parametric ML model instances. Each non-parametric ML model instance developed at each data processing node is shared with each of remaining data processing nodes of the plurality of data processing nodes. Each non-parametric ML model instance is processed through a trainable parametric combinator to generate a composite model at each of the plurality of data processing nodes. The composite model is trained at each of the plurality of data processing nodes, over the respective local dataset, using Swarm learning to obtain trained composite models.

Abstract: A system and a method for training non-parametric Machine Learning (ML) model instances in a collaborative manner is disclosed. A non-parametric ML model instance is trained at each of a plurality of data processing nodes to obtain a plurality of non-parametric ML model instances. Each non-parametric ML model instance developed at each data processing node is shared with each of remaining data processing nodes of the plurality of data processing nodes. Each non-parametric ML model instance is processed through a trainable parametric combinator to generate a composite model at each of the plurality of data processing nodes. The composite model is trained at each of the plurality of data processing nodes, over the respective local dataset, using Swarm learning to obtain trained composite models.