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Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters
The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to cent...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206243/ http://dx.doi.org/10.1007/978-3-030-47426-3_27 |
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author | Soliman, Amira Girdzijauskas, Sarunas Bouguelia, Mohamed-Rafik Pashami, Sepideh Nowaczyk, Slawomir |
author_facet | Soliman, Amira Girdzijauskas, Sarunas Bouguelia, Mohamed-Rafik Pashami, Sepideh Nowaczyk, Slawomir |
author_sort | Soliman, Amira |
collection | PubMed |
description | The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to central location for analysis causes not only many privacy concerns, but also communication overhead. Therefore, in certain situations machine learning models need to be trained in a collaborative and decentralized manner, similar to the way the data is originally generated without requiring any central authority for data or model aggregation. This paper presents a decentralized and adaptive k-means algorithm that clusters data from multiple sources organized in peer-to-peer networks. Our algorithm allows peers to reach an approximation of the global model without sharing any raw data. Most importantly, we address the challenge of decentralized clustering with skewed non-IID data and asynchronous computations by integrating HyperLogLog counters with k-means algorithm. Furthermore, our clustering algorithm allows nodes to individually determine the number of clusters that fits their local data. Results using synthetic and real-world datasets show that our algorithm outperforms state-of-the-art decentralized k-means algorithms achieving accuracy gain that is up-to 36%. |
format | Online Article Text |
id | pubmed-7206243 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72062432020-05-08 Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters Soliman, Amira Girdzijauskas, Sarunas Bouguelia, Mohamed-Rafik Pashami, Sepideh Nowaczyk, Slawomir Advances in Knowledge Discovery and Data Mining Article The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to central location for analysis causes not only many privacy concerns, but also communication overhead. Therefore, in certain situations machine learning models need to be trained in a collaborative and decentralized manner, similar to the way the data is originally generated without requiring any central authority for data or model aggregation. This paper presents a decentralized and adaptive k-means algorithm that clusters data from multiple sources organized in peer-to-peer networks. Our algorithm allows peers to reach an approximation of the global model without sharing any raw data. Most importantly, we address the challenge of decentralized clustering with skewed non-IID data and asynchronous computations by integrating HyperLogLog counters with k-means algorithm. Furthermore, our clustering algorithm allows nodes to individually determine the number of clusters that fits their local data. Results using synthetic and real-world datasets show that our algorithm outperforms state-of-the-art decentralized k-means algorithms achieving accuracy gain that is up-to 36%. 2020-04-17 /pmc/articles/PMC7206243/ http://dx.doi.org/10.1007/978-3-030-47426-3_27 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Soliman, Amira Girdzijauskas, Sarunas Bouguelia, Mohamed-Rafik Pashami, Sepideh Nowaczyk, Slawomir Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters |
title | Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters |
title_full | Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters |
title_fullStr | Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters |
title_full_unstemmed | Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters |
title_short | Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters |
title_sort | decentralized and adaptive k-means clustering for non-iid data using hyperloglog counters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206243/ http://dx.doi.org/10.1007/978-3-030-47426-3_27 |
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