Resource Optimization for Blockchain-Based Federated Learning in Mobile Edge Computing

dc.contributor.authorWang, Zhilin
dc.contributor.authorHu, Qin
dc.contributor.authorXiong, Zehui
dc.contributor.departmentComputer and Information Science, Purdue School of Science
dc.date.accessioned2025-04-11T19:58:01Z
dc.date.available2025-04-11T19:58:01Z
dc.date.issued2024-05
dc.description.abstractWith the booming of mobile edge computing (MEC) and blockchain-based blockchain-based federated learning (BCFL), more studies suggest deploying BCFL on edge servers. In this case, edge servers with restricted resources face the dilemma of serving both mobile devices for their offloading tasks and the BCFL system for model training and blockchain consensus without sacrificing the service quality to any side. To address this challenge, this article proposes a resource allocation scheme for edge servers to provide optimal services at the minimum cost. Specifically, we first analyze the energy consumption of the MEC and BCFL tasks, considering the completion time of each task as the service quality constraint. Then, we model the resource allocation challenge into a multivariate, multiconstraint, and convex optimization problem. While solving the problem in a progressive manner, we design two algorithms based on the alternating direction method of multipliers (ADMMs) in both homogeneous and heterogeneous situations, where equal and on-demand resource distribution strategies are, respectively, adopted. The validity of our proposed algorithms is proved via rigorous theoretical analysis. Moreover, the convergence and efficiency of our proposed resource allocation schemes are evaluated through extensive experiments.
dc.eprint.versionAuthor's manuscript
dc.identifier.citationWang, Z., Hu, Q., Xiong, Z., Liu, Y., & Niyato, D. (2024). Resource Optimization for Blockchain-Based Federated Learning in Mobile Edge Computing. IEEE Internet of Things Journal, 11(9), 15166–15178. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2023.3347524
dc.identifier.urihttps://hdl.handle.net/1805/47015
dc.language.isoen
dc.publisherIEEE
dc.relation.isversionof10.1109/JIOT.2023.3347524
dc.relation.journalIEEE Internet of Things Journal
dc.rightsPublisher Policy
dc.sourceArXiv
dc.subjectblockchain
dc.subjectfederated learning
dc.subjectresource allocation
dc.titleResource Optimization for Blockchain-Based Federated Learning in Mobile Edge Computing
dc.typeArticle
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