Knowledge Is at the Edge! How to Search in Distributed Machine Learning Models

Abstract

Abstract. With the advent of the internet of things and industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95 % accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.

Approach to M-learning Acceptance Among University Students

“…As noted by Kukulska-Hulme (2010) "learning is open to all when it is inclusive, and mobile technologies are a powerful means of opening up learning to all those who might otherwise remain at the margins of education" (p. 184). A new era of distributed learning is therefore being established with the progressive development of machine learning in mobile devices (Bach, Tariq, Mayer, & Rothermel, 2017).…”

Edge Intelligence: Empowering Intelligence to the Edge of Network

“…Works on D2E offloading strategy [362]- [369] also adopt such operation, which could further reduce latency and the dependency on cellular network. Most works on D2D offloading strategy [370]- [377] focus on smart home scenarios, where IoT devices, smartwatches and smartphones collaboratively perform training/inference tasks. Hybrid offloading schemes [378]- [380] have the strongest ability of adaptiveness, which makes the most of all the available resources.…”

“…Distributed solo learning enables edge devices or edge servers to train models with local data. Consequently, each propose a routing strategy to forward the queries to devices that have the specific knowledge [377]. The strategy is similar to the routing strategy in TCP/IP networks.…”