“Chatty Devices” and edge-based activity classification

Abstract

With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a “chatty" factory involves creating products which are able to send data back to the manufacturing/production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may different significantly from actual usage. Understanding how this data can be used to support continuous product refinement, and how the manufacturing process can be dynamically adapted based on the availability of this data provides a number of opportunities. We describe how data collected on product use can be used to: (i) classify product use; (ii) associate a label with product use using unsupervised learning—making use of edge-based analytics; (iii) transmission of this data to a cloud environment where labels can be compared across different products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy).

Data-Quality Based Scheduling for Federated Edge Learning

“…with i ∈ {elements diversity, dataset size, age} Depending on the application, other quality measures can also be used. For instance, in image classification, measures regarding image quality (e.g., blur) can heavily affect the learning [24,25]. Such measures can be evaluated on a subset of images and normalized for a seamless integration in the index.…”