Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 16, Pages 12806-12825Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3072611
Keywords
Blockchain; Servers; Edge computing; Artificial intelligence; Training; Security; Computational modeling; Blockchain; edge computing; federated learning (FL); Internet of Things (IoT); privacy; security
Categories
Funding
- CSIRO Data61, Australia
- U.S. National Science Foundation [CCF-1908308]
- National Natural Science Foundation of China [61872184]
- National Research Foundation of Korea (NRF) - Korean Government (MSIT) [NRF2019R1C1C1006143]
- National Research Foundation of Korea [2019R1C1C1006143] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The article discusses the concept of FLchain in MEC networks, focusing on privacy protection, security, cross-device collaboration, and resource allocation. FLchain integrates FL and blockchain technology, presenting a promising paradigm for intelligent MEC networks.
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.
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