Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

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 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.

A Federated Learning Framework for Enforcing Traceability in Manufacturing Processes

“…Federated learning schemes have shown promising results when fed with large and distributed datasets created by various sensors and IoT devices in industrial applications [20], [21], [22]. A federated learning approach might be considered as an effective solution for combining heterogeneous datasets, especially when emanating from different production lines in a pharma manufacturing environment.…”

Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends

“…In this context, a promising ledger technology is the blockchain, which refers to a public and trusted ledger, operating on a peerto-peer network without any third party being involved. Thus, WFL could benefit from blockchain in terms of security and privacy improvement, since it eliminates the need of a central server, while the participants are collaboratively building the global learning model through a consensus mechanism [20]. Finally, FL can facilitate the construction of digital twin models of IoT devices, which has also been recognized as a research priority for the NGIoT [19], [21].…”