Multi-machine based collaborative learning

Abstract

Individual nodes (e.g., edge machines) in an overlay network each build local machine learning (ML) models associated with a particular behavior of interest. Through a communication mechanism, nodes exchange some portion of their ML models between or among each other. The portion of the local model that is exchanged with one or more other nodes encodes or encapsulates relevant knowledge (learned at the source node) for the particular behavior of interest; in this manner, relevant transfer learning is enabled such that individual node models become smarter. Sets of machines that collaborate converge their models toward a solution that is then used to facilitate another overlay network function or optimization. The local knowledge exchange among the nodes creates an emergent behavioral profile used to control the edge machine behavior. Example functions managed with this ML front-end include predictive pre-fetching, anomaly detection, image management, forecasting to allocate resources, and others.