Literature by the same author
plus at Google Scholar

Bibliografische Daten exportieren
 

Knowledge is at the edge! How to search in distributed machine learning models

Title data

Bach, Thomas ; Tariq, Muhammad Adnan ; Mayer, Ruben ; Rothermel, Kurt:
Knowledge is at the edge! How to search in distributed machine learning models.
In: On the Move to Meaningful Internet Systems. OTM 2017 Conferences : Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings. Part I. - Cham : Springer , 2017 . - pp. 410-428 . - (Lecture Notes in Computer Science ; 10573 )
ISBN 978-3-319-69462-7
DOI: https://doi.org/10.1007/978-3-319-69462-7_27

Abstract in another language

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.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: knowledge retrieval; distributed knowledge; query routing
Institutions of the University: Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Data Systems
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Data Systems > Chair Data Systems - Univ.-Prof. Dr. Ruben Mayer
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 25 Apr 2023 13:42
Last Modified: 05 Feb 2024 07:42
URI: https://eref.uni-bayreuth.de/id/eprint/76026