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Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness [article]

Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
<span title="2021-12-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2021-11-24">2021</span> <span class="release-stage" >pre-print</span>
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<span title="2021-09-28">2021</span> <span class="release-stage" >pre-print</span>
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<span title="2021-07-30">2021</span> <span class="release-stage" >pre-print</span>
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Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising solution to privacy-preserving data processing for millions of smart edge devices, leverages distributed computing  ...  and trustworthiness.  ...  Available: Processing - 19th International Conference, ICA3PP 2019, Melbourne, http://proceedings.mlr.press/v54/mcmahan17a.html VIC, Australia, December  ... 
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Citation

Yuwei Sun, Hideya Ochiai, Hiroshi Esaki. "Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness." arXiv (2021)