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 data communication overheads. 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 exposing their 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 topics in FLchain design, including
communication cost, resource allocation, incentive mechanism, security and
privacy protection. The key solutions for FLchain design are provided, and the
lessons learned as well as 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.

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Author Of this post: <a href="">Dinh C. Nguyen</a>, <a href="">Ming Ding</a>, <a href="">Quoc-Viet Pham</a>, <a href="">Pubudu N. Pathirana</a>, <a href="">Long Bao Le</a>, <a href="">Aruna Seneviratne</a>, <a href="">Jun Li</a>, <a href="">Dusit Niyato</a>, <a href="">H. Vincent Poor</a>

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