Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach

Moqbel Hamood, Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Clustered Federated Multi-task Learning (CFL) has gained considerable attention as an effective strategy for over-coming statistical challenges, particularly when dealing with non-independent and identically distributed (non-IID) data across multiple users. However, much of the existing research on CFL operates under the unrealistic premise that devices have access to accurate ground-truth labels. This assumption becomes especially problematic in, especially hierarchical wireless networks (HWNs), where edge networks contain a large amount of unlabeled data, resulting in slower convergence rates and increased processing times-particularly when dealing with two layers of model aggregation. To address these issues, we introduce a novel frame-work-Clustered Federated Semi-Supervised Learning (CFSL), designed for more realistic HWN scenarios. Our approach leverages a best-performing specialized model algorithm, wherein each device is assigned a specialized model that is highly adept at generating accurate pseudo-labels for unlabeled data, even when the data stems from diverse environments. We validate the efficacy of CFSL through extensive experiments, comparing it with existing methods highlighted in recent literature. Our numerical results demonstrate that CFSL significantly improves upon key metrics such as testing accuracy, labeling accuracy, and labeling latency under varying proportions of labeled and unlabeled data while also accommodating the non-IID nature of the data and the unique characteristics of wireless edge networks.

Original languageEnglish
Title of host publication2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350303582
DOIs
Publication statusPublished - 24 Apr 2024
Event25th IEEE Wireless Communications and Networking Conference, WCNC 2024 - Dubai, United Arab Emirates
Duration: 21 Apr 202424 Apr 2024

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511

Conference

Conference25th IEEE Wireless Communications and Networking Conference, WCNC 2024
Country/TerritoryUnited Arab Emirates
CityDubai
Period21/04/2424/04/24

Keywords

  • Clustered federated learning (CFL)
  • Hierarchical wireless networks
  • Semi-supervised learning (SSL)
  • Specialized models

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  • EX-QNRF-NPRPS-37: Secure Federated Edge Intelligence Framework for AI-driven 6G Applications

    Abdallah, M. M. (Lead Principal Investigator), Al Fuqaha, A. (Principal Investigator), Hamood, M. (Graduate Student), Aboueleneen, N. (Graduate Student), Student-1, G. (Graduate Student), Student-2, G. (Graduate Student), Fellow-1, P. D. (Post Doctoral Fellow), Assistant-1, R. (Research Assistant), Mohamed, D. A. (Principal Investigator), Mahmoud, D. M. (Principal Investigator), Al-Dhahir, P. N. (Principal Investigator) & Khattab, P. T. (Principal Investigator)

    19/04/2130/08/24

    Project: Applied Research

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