Edge-Assisted Opportunistic Federated Learning for Distributed IoT Systems

Noor Khial, Alaa Awad Abdellatif, Amr Mohamed, Aiman Erbad, Carla Fabiana Chiasserini

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

1 Citation (Scopus)

Abstract

The paper introduces Opportunistic Federated Learning (OFL) as an approach to enhance the efficiency of distributed learning in intelligent IoT systems. OFL allows any node in the network to initiate a learning task and collaboratively use local resources. The framework enables nodes to adapt configurations based on circumstances, optimizing resource utilization. Hence, this paper proposes a reliable node selection mechanism that accommodates the dynamic nature of local data and computing resources. Incentives for participating nodes are explored through a peer-to-peer communication using the Bertrand game to determine optimal pricing strategies. Results demonstrate the Nash equilibrium of the game-based incentive mechanism in a realistic FL setup.

Original languageEnglish
Title of host publication2024 IEEE 21st Consumer Communications and Networking Conference, CCNC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages604-605
Number of pages2
ISBN (Electronic)9798350304572
DOIs
Publication statusPublished - 18 Mar 2024
Event21st IEEE Consumer Communications and Networking Conference, CCNC 2024 - Las Vegas, United States
Duration: 6 Jan 20249 Jan 2024

Publication series

NameProceedings - IEEE Consumer Communications and Networking Conference, CCNC
ISSN (Print)2331-9860

Conference

Conference21st IEEE Consumer Communications and Networking Conference, CCNC 2024
Country/TerritoryUnited States
CityLas Vegas
Period6/01/249/01/24

Keywords

  • Distributed learning
  • Nash equilibrium
  • edge computing
  • game theory
  • reputation analysis

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