{"id":1535,"date":"2025-01-30T07:03:16","date_gmt":"2025-01-30T07:03:16","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/01\/30\/2501-17512\/"},"modified":"2025-01-30T07:03:16","modified_gmt":"2025-01-30T07:03:16","slug":"2501-17512","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/01\/30\/2501-17512\/","title":{"rendered":"A Survey on Cluster-based Federated Learning"},"content":{"rendered":"

A Survey on Cluster-based Federated Learning
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arXiv:2501.17512v1 Announce Type: new
\nAbstract: As the industrial and commercial use of Federated Learning (FL) has expanded, so has the need for optimized algorithms.
\n In settings were FL clients’ data is non-independently and identically distributed (non-IID) and with highly heterogeneous distributions, the baseline FL approach seems to fall short. To tackle this issue, recent studies, have looked into personalized FL (PFL) which relaxes the implicit single-model constraint and allows for multiple hypotheses to be learned from the data or local models. Among the personalized FL approaches, cluster-based solutions (CFL) are particularly interesting whenever it is clear -through domain knowledge -that the clients can be separated into groups.
\n In this paper, we study recent works on CFL, proposing: i) a classification of CFL solutions for personalization; ii) a structured review of literature iii) a review of alternative use cases for CFL. CCS Concepts: $bullet$ General and reference $rightarrow$ Surveys and overviews; $bullet$ Computing methodologies $rightarrow$ Machine learning; $bullet$ Information systems $rightarrow$ Clustering; $bullet$ Security and privacy $rightarrow$ Privacy-preserving protocols.<\/div>\n

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\n Omar El-Rifai (CIS-ENSMSE), Michael Ben Ali (IRIT), Imen Megdiche (IRIT, IRIT-SIG, INUC), Andr’e Peninou (IRIT, IRIT-SIG, UT2J), Olivier Teste (IRIT-SIG, IRIT, UT2J, UT)
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A Survey on Cluster-based Federated Learning arXiv:2501.17512v1 Announce Type: new Abstract: As the industrial and commercial use of Federated Learning (FL) has expanded, so has the need for optimized algorithms. In settings were FL clients’ data is non-independently and identically distributed (non-IID) and with highly heterogeneous distributions, the baseline FL approach seems to fall short. […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,112],"tags":[1550,1549,199],"class_list":["post-1535","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-fl","tag-irit","tag-learning"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1535"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=1535"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1535\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=1535"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=1535"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=1535"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}