Verma, DC;
White, G;
Julier, S;
Pasteris, S;
Chakraborty, S;
Cirincione, G;
(2019)
Approaches to address the Data Skew Problem in Federated Learning.
In: Pham, T, (ed.)
SPIE 11006.
SPIE
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Approaches to address the Data Skew Problem in Federated Learning |
| Event: | Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications |
| Location: | Baltimore, MD |
| Dates: | 15 April 2019 - 17 April 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2519621 |
| Publisher version: | https://doi.org/10.1117/12.2519621 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10125624 |
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