Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
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DOI：
10.1109/tai.2021.3133819
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Yuwei Sun;H. Ochiai;H. Esaki
中科院分区：
文献类型：
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作者：
Yuwei Sun;H. Ochiai;H. Esaki
更广泛的覆盖范围和更好的解决方案，以减少5G的延迟，需要它与多路访问边缘计算（MEC）技术相结合。分散式深度学习（Decentralized Deep Learning，简称DAI），如联邦学习（Federated Learning）和群学习（Swarm Learning），作为数百万智能边缘设备的隐私保护数据处理的一种有前途的解决方案，利用了本地客户端网络内多层神经网络的分布式计算，而不会泄露原始的本地训练数据。值得注意的是，在金融和医疗保健等行业，交易和个人医疗记录的敏感数据会被谨慎维护，DDL可以促进这些机构之间的协作，以提高训练模型的性能，同时保护参与客户的数据隐私。在这篇调查论文中，我们展示了分布式学习的技术基础，这些技术基础通过分散式学习使社会各界受益。此外，我们还从通信效率和可信度的新角度概述了物联网的挑战和最相关的解决方案，从而全面概述了该领域的当前最新技术水平。
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. 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 of multi-layer neural networks within the networking of local clients, whereas, without disclosing the original local training data. Notably, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL can facilitate the collaboration among these institutes to improve the performance of trained models while protecting the data privacy of participating clients. In this survey paper, we demonstrate the technical fundamentals of DDL that benefit many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of the current state-of-the-art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.