FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment. (arXiv:2205.12038v1 [cs.LG])

Along with the popularity of Artificial Intelligence (AI) and
Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily
increasing attentions as a promising distributed machine learning paradigm,
which enables the training of a central model on for numerous decentralized
devices without exposing their privacy. However, due to the biased data
distributions on involved devices, FL inherently suffers from low
classification accuracy in non-IID scenarios. Although various device grouping
method have been proposed to address this problem, most of them neglect both …

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