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

TL;DR

This paper addresses the challenge of searching for specific knowledge within distributed machine learning models at the network edge, proposing a method that improves search accuracy while preserving privacy and reducing latency.

Contribution

It introduces an entropy-based forwarding strategy for querying distributed models, achieving high accuracy in a real-world urban mobility scenario.

Findings

Over 95% search accuracy in urban mobility data

Effective knowledge retrieval in distributed models

Preserves user privacy by local data processing

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.