On a Framework for Federated Cluster Analysis
Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, similar to federated clustering, is still less explore...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10455 |
| _version_ | 1797475551711068160 |
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| author | Morris Stallmann Anna Wilbik |
| author_facet | Morris Stallmann Anna Wilbik |
| author_sort | Morris Stallmann |
| collection | DOAJ |
| description | Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, similar to federated clustering, is still less explored. In this paper, we introduce a federated clustering framework that solves three challenges: determine the number of global clusters in a federated dataset, obtain a partition of the data via a federated fuzzy <i>c</i>-means algorithm, and validate the clustering through a federated fuzzy Davies–Bouldin index. The complete framework is evaluated through numerical experiments on artificial and real-world datasets. The observed results are promising, as in most cases the federated clustering framework’s results are consistent with its nonfederated equivalent. Moreover, we embed an alternative federated fuzzy <i>c</i>-means formulation into our framework and observe that our formulation is more reliable in case the data are noni.i.d., while the performance is on par in the i.i.d. case. |
| first_indexed | 2024-03-09T20:46:45Z |
| format | Article |
| id | doaj.art-7a7c7ae520a74185beddd372c1b31483 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:46:45Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7a7c7ae520a74185beddd372c1b314832023-11-23T22:44:58ZengMDPI AGApplied Sciences2076-34172022-10-0112201045510.3390/app122010455On a Framework for Federated Cluster AnalysisMorris Stallmann0Anna Wilbik1Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, 6229 EN Maastricht, The NetherlandsDepartment of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, 6229 EN Maastricht, The NetherlandsFederated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, similar to federated clustering, is still less explored. In this paper, we introduce a federated clustering framework that solves three challenges: determine the number of global clusters in a federated dataset, obtain a partition of the data via a federated fuzzy <i>c</i>-means algorithm, and validate the clustering through a federated fuzzy Davies–Bouldin index. The complete framework is evaluated through numerical experiments on artificial and real-world datasets. The observed results are promising, as in most cases the federated clustering framework’s results are consistent with its nonfederated equivalent. Moreover, we embed an alternative federated fuzzy <i>c</i>-means formulation into our framework and observe that our formulation is more reliable in case the data are noni.i.d., while the performance is on par in the i.i.d. case.https://www.mdpi.com/2076-3417/12/20/10455federated learningframeworkcluster analysiscluster number determinationfederated fuzzy Davies–Bouldin indexfederated cluster validation metric |
| spellingShingle | Morris Stallmann Anna Wilbik On a Framework for Federated Cluster Analysis Applied Sciences federated learning framework cluster analysis cluster number determination federated fuzzy Davies–Bouldin index federated cluster validation metric |
| title | On a Framework for Federated Cluster Analysis |
| title_full | On a Framework for Federated Cluster Analysis |
| title_fullStr | On a Framework for Federated Cluster Analysis |
| title_full_unstemmed | On a Framework for Federated Cluster Analysis |
| title_short | On a Framework for Federated Cluster Analysis |
| title_sort | on a framework for federated cluster analysis |
| topic | federated learning framework cluster analysis cluster number determination federated fuzzy Davies–Bouldin index federated cluster validation metric |
| url | https://www.mdpi.com/2076-3417/12/20/10455 |
| work_keys_str_mv | AT morrisstallmann onaframeworkforfederatedclusteranalysis AT annawilbik onaframeworkforfederatedclusteranalysis |