Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

【Author】 Nguyen, Dinh C.; Ding, Ming; Quoc-Viet Pham; Pathirana, Pubudu N.; Le, Long Bao; Seneviratne, Aruna; Li, Jun; Niyato, Dusit; Poor, H. Vincent

【Source】IEEE INTERNET OF THINGS JOURNAL

【Abstract】Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high overhead of raw data communications. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without directly exposing their underlying data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main issues in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions and the lessons learned along with the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

【Keywords】Blockchain; Servers; Edge computing; Artificial intelligence; Training; Security; Computational modeling; Blockchain; edge computing; federated learning (FL); Internet of Things (IoT); privacy; security

【标题】联邦学习在边缘计算中遇到区块链：机遇与挑战

【摘要】移动边缘计算 (MEC) 被认为是一种很有前途的范式，可以处理无处不在的移动设备产生的大量数据，从而在人工智能 (AI) 的帮助下实现智能服务。传统上，人工智能技术通常需要在单个实体（例如 MEC 服务器）中进行集中式数据收集和训练，但由于数据隐私问题和原始数据通信的高开销，现在已成为一个弱点。在这种情况下，联邦学习（FL）被提出来提供协作数据训练解决方案，通过协调多个移动设备来训练一个共享的人工智能模型，而不直接暴露它们的底层数据，这享有相当大的隐私增强。为了提高 FL 实施的安全性和可扩展性，区块链作为一种账本技术对于实现去中心化 FL 训练具有吸引力，而无需任何中央服务器。特别是，FL 和区块链的集成产生了一种新的范式，称为 FLchain，它有可能将智能 MEC 网络转变为去中心化、安全和增强隐私的系统。本文概述了基本概念，并探讨了 FLchain 在 MEC 网络中的机会。我们确定了 FLchain 设计中的几个主要问题，包括通信成本、资源分配、激励机制、安全和隐私保护。还讨论了关键解决方案和经验教训以及展望。然后，我们研究了 FLchain 在流行的 MEC 领域的应用，例如边缘数据共享、边缘内容缓存和边缘众感。最后，还强调了重要的研究挑战和未来方向。

【关键词】区块链；服务器；边缘计算；人工智能;训练;安全;计算建模；区块链；边缘计算；联邦学习（FL）；物联网（IoT）；隐私;安全

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】两者结合

【影响因子】10.238

【翻译者】石东瑛