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With the advent of the Internet of Things and Industry 4.0 an enormous amountof data is produced at the edge of the network. Due to a lack of computingpower, this data is currently send to the cloud where centralized machinelearning models are trained to derive higher level knowledge. With the recentdevelopment of specialized machine learning hardware for mobile devices, a newera of distributed learning is about to begin that raises a new researchquestion: How can we search in distributed machine learning models? Machinelearning at the edge of the network has many benefits, such as low-latencyinference and increased privacy. Such distributed machine learning models canalso learn personalized for a human user, a specific context, or applicationscenario. As training data stays on the devices, control over possiblysensitive data is preserved as it is not shared with a third party. This newform of distributed learning leads to the partitioning of knowledge betweenmany devices which makes access difficult. In this paper we tackle the problemof finding specific knowledge by forwarding a search request (query) to adevice that can answer it best. To that end, we use a entropy based qualitymetric that takes the context of a query and the learning quality of a deviceinto account. We show that our forwarding strategy can achieve over 95%accuracy in a urban mobility scenario where we use data from 30 000 peoplecommuting in the city of Trento, Italy.

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