Method and system for incremental training of machine learning models on edge devices

Abstract

A method and system for incremental training of machine learning (ML) models on edge devices, is disclosed. A base version of ML model is received by a first device of the peer to peer network of devices. The base version of the ML model is incremental trained in real-time by updating weights associated with the parameters of the ML model during a predetermined window of time. The ML model are devoid of an underlying event data used to generate the incremental version of the ML model. The updated weights associated with parameters of the incrementally trained ML model and/or the respective parameters are shared by each edge device with other edge devices. The edge devices update their respective ML models based on the received updated weights and/or parameters upon subsequent events at the edge devices and the updated version of the ML models are further incrementally trained.