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Related Experiment Video

Updated: Jun 11, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks.

G Nalinipriya1, E Laxmi Lydia2, S Rama Sree3

  • 1Department of Information Technology, Saveetha Engineering College, Chennai, Tamilnadu, 602 105, India.

Scientific Reports
|September 28, 2024
PubMed
Summary

This study introduces a new federated learning (FL) method for IoT networks, optimizing device lifetime and energy efficiency. The LM-ODAGFL technique uses a Directed Acyclic Graph (DAG) and Archimedes Optimization Algorithm (AOA) to reduce energy consumption and training loss.

Keywords:
Archimedes optimization algorithmEnergy consumptionFederated learningInternet of thingsLifetime maximization

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Wireless Communication

Background:

  • Federated learning (FL) enables distributed on-device computation for enhanced efficiency.
  • Managing large-scale, resource-constrained IoT networks with FL presents significant challenges.
  • Device asynchrony and high energy consumption are key issues in current FL applications.

Purpose of the Study:

  • To introduce a novel technique, Lifetime Maximization using Optimal Directed Acyclic Graph Federated Learning (LM-ODAGFL), for energy-effective IoT networks.
  • To address device asynchrony and minimize resource usage in FL using a Directed Acyclic Graph (DAG) model.
  • To optimize the FL model and reduce user energy consumption via the Archimedes Optimization Algorithm (AOA).

Main Methods:

  • The LM-ODAGFL technique integrates FL with metaheuristic optimization algorithms.
  • A Directed Acyclic Graph (DAG) model is employed to manage device asynchrony within the FL framework.
  • The Archimedes Optimization Algorithm (AOA) is utilized to optimize the DAG model, focusing on energy reduction and training loss minimization.

Main Results:

  • The LM-ODAGFL technique demonstrated significantly lower energy consumption compared to SDAGFL and ESDAGFL.
  • Energy consumption per round for LM-ODAGFL ranged from 0.373 to 0.485 kJ on the FMNIST-Clustered dataset.
  • On the Poets dataset, LM-ODAGFL consumed 16.27 to 20.34 kJ per round, outperforming existing methods.

Conclusions:

  • The proposed LM-ODAGFL technique offers a superior, energy-efficient solution for federated learning in IoT communication networks.
  • The integration of DAG and AOA effectively tackles device asynchrony and optimizes resource utilization.
  • Experimental results validate the enhanced performance of LM-ODAGFL in terms of energy savings and model efficiency.