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Gossip Learning: Off the Beaten Path
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-0223-8907
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The growing computational demands of model training tasks and the increased privacy awareness of consumers call for the development of new techniques in the area of machine learning. Fully decentralized approaches have been proposed, but are still in early research stages. This study analyses gossip learning, one of these state-of-the-art decentralized machine learning protocols, which promises high scalability and privacy preservation, with the goal of assessing its applicability to realworld scenarios.

Previous research on gossip learning presents strong and often unrealistic assumptions on the distribution of the data, the communication speeds of the devices and the connectivity among them. Our results show that lifting these requirements can, in certain scenarios, lead to slow convergence of the protocol or even unfair bias in the produced models. This paper identifies the conditions in which gossip learning can and cannot be applied, and introduces extensions that mitigate some of its limitations.

Place, publisher, year, edition, pages
2019.
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-263863OAI: oai:DiVA.org:kth-263863DiVA, id: diva2:1370825
Conference
2019 IEEE International Conference on Big Data (IEEE Big Data 2019), December 9-12, 2019, Los Angeles, CA, USA
Note

Accepted paper. QC 20191122

Available from: 2019-11-18 Created: 2019-11-18 Last updated: 2019-11-22Bibliographically approved

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Giaretta, LodovicoGirdzijauskas, Sarunas

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