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Mar, 2021
异质性获胜:一次性联邦聚类
Heterogeneity for the Win: One-Shot Federated Clustering
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Don Kurian Dennis, Tian Li, Virginia Smith
TL;DR
本文探讨了无监督联邦学习的独特挑战和机遇,提出了一种基于 Lloyd 方法的一次性联邦聚类方案 k-FED,并在实验中验证了它的可行性和实用性。
Abstract
In this work, we explore the unique challenges -- and opportunities -- of
unsupervised federated learning
(FL). We develop and analyze a one-shot
federated clustering scheme
, $k$-FED, based on the widely-used Llo
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