Consensus Driven Learning

Systems and methods are provided for consensus driven learning (CDL) using machine learning (ML) to enable devices to learn a model on a data set that is distributed over several computational nodes in a decentralized manner. In an embodiment, local models are trained on local data and share model parameters in an asynchronous, decentralized, and distributed manner that imposes minimal restrictions on the topology of a communications network. Systems and methods using CDL in accordance with embodiments of the present disclosure do not require a central server to coordinate models like most conventional technologies, high bandwidth, or highly robust communication architecture between nodes.