{"id":705,"date":"2024-12-20T07:02:41","date_gmt":"2024-12-20T07:02:41","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2024\/12\/20\/2412-14226\/"},"modified":"2024-12-20T07:02:41","modified_gmt":"2024-12-20T07:02:41","slug":"2412-14226","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2024\/12\/20\/2412-14226\/","title":{"rendered":"FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning"},"content":{"rendered":"<p>    FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2412.14226v1 Announce Type: cross<br \/>\nAbstract: Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-preserving manner. In this paper, we propose textit{FedSTaS}, a client and data-level sampling method inspired by textit{FedSTS} and textit{FedSampling}. In each federated learning round, textit{FedSTaS} stratifies clients based on their compressed gradients, re-allocate the number of clients to sample using an optimal Neyman allocation, and sample local data from each participating clients using a data uniform sampling strategy. Experiments on three datasets show that textit{FedSTaS} can achieve higher accuracy scores than those of textit{FedSTS} within a fixed number of training rounds.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Jordan Slessor, Dezheng Kong, Xiaofen Tang, Zheng En Than, Linglong Kong<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2412.14226\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning arXiv:2412.14226v1 Announce Type: cross Abstract: Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how [&hellip;]<\/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":[815,814,813],"class_list":["post-705","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-stat-ml","tag-client","tag-fedstas","tag-textit"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/705"}],"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=705"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/705\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=705"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=705"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=705"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}