Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges

By Dinh C. Nguyen, Ming Ding, Quoc-Viet Pham, Pubudu N. Pathirana, Long Bao, Le Aruna, Jun Seneviratne, Dusit Li, F. I. H. V. Niyato, Life Fellow Ieee Poor and 3 more. IEEE Internet of Things Journal, 2021

595 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a025ad05916ddf3281a2bca0cdffbaed99a2c9dd

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Citations in the corpus

Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, unknown): “This synchronously distributed training pattern of FL leads to the challenge referred to as system heterogeneity [73].”
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “Recently, FL has emerged as a new AI concept that leverages on-device processing power and improves the user data privacy [209]–[211].”
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges (2022, unknown): “In contrast to SFL protocols, Blockchain based FL enables a decentralized FL environment without single point of failure risks and improved scalability [153, 154].”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “The combination of federated learning and blockchain is also a potential solution towards privacy protection [200] and currently still in its very early stage.”
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022, methodology): “…and distributed security mechanism designs can be designed to establish user authentication and access control, model/data integrity, and mutual platform veriﬁcation [8]. d) The use of federated learning, blockchain, or the integration of both (e.g., [37]) can further improve security/privacy.”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “[52] proposed FLchain to meet the requirements for servers and communication resources in the blockchain by mobile edge computing servers.”
Federated Learning for Smart Healthcare: A Survey (2021, background): “Although the FL concept can provide privacy protection by allowing users to keep their data at local sites during the training, it has been proven that the model updates might still contain health user-related information such as data features and image resolution that can be re-constructed by the c”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “• Enhanced Learning Quality: By attracting much computation resources and diverse datasets from a network of IoT devices, FL has the potential to enhance the convergence rate of the overall training process and achieve better learning accuracy rates [8], which might not be achieved by using centrali”