dc.contributor.author | Liu, X | |
dc.contributor.author | Deng, Y | |
dc.contributor.author | Nallanathan, A | |
dc.contributor.author | Bennis, M | |
dc.date.accessioned | 2024-07-12T07:18:42Z | |
dc.date.available | 2024-07-12T07:18:42Z | |
dc.date.issued | 2023-11-07 | |
dc.identifier.citation | X. Liu, Y. Deng, A. Nallanathan and M. Bennis, "Federated Learning and Meta Learning: Approaches, Applications, and Directions," in IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 571-618, Firstquarter 2024, doi: 10.1109/COMST.2023.3330910. keywords: {Metalearning;Task analysis;Data models;Tutorials;Surveys;Data privacy;Servers;Centralized learning;distributed learning;federated learning;meta learning;federated meta learning;wireless networks}, | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/98039 | |
dc.description.abstract | Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications. | en_US |
dc.format.extent | 571 - 618 | |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | IEEE Communications Surveys and Tutorials | |
dc.rights | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.title | Federated Learning and Meta Learning: Approaches, Applications, and Directions | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/COMST.2023.3330910 | |
pubs.issue | 1 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 26 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
rioxxterms.funder.project | b215eee3-195d-4c4f-a85d-169a4331c138 | en_US |