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Proceedings Paper

Approaches to address the data skew problem in federated learning
Author(s): Dinesh C. Verma; Graham White; Simon Julier; Stepehen Pasteris; Supriyo Chakraborty; Greg Cirincione
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Paper Abstract

A Federated Learning approach consists of creating an AI model from multiple data sources, without moving large amounts of data across to a central environment. Federated learning can be very useful in a tactical coalition environment, where data can be collected individually by each of the coalition partners, but network connectivity is inadequate to move the data to a central environment. However, such data collected is often dirty and imperfect. The data can be imbalanced, and in some cases, some classes can be completely missing from some coalition partners. Under these conditions, traditional approaches for federated learning can result in models that are highly inaccurate. In this paper, we propose approaches that can result in good machine learning models even in the environments where the data may be highly skewed, and study their performance under different environments.

Paper Details

Date Published: 10 May 2019
PDF: 16 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061I (10 May 2019); doi: 10.1117/12.2519621
Show Author Affiliations
Dinesh C. Verma, IBM Thomas J. Watson Research Ctr. (United States)
Graham White, IBM United Kingdom Ltd. (United Kingdom)
Simon Julier, Univ. College London (United Kingdom)
Stepehen Pasteris, Univ. College London (United Kingdom)
Supriyo Chakraborty, IBM Thomas J. Watson Research Ctr. (United States)
Greg Cirincione, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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