Communication-Aware Optimizers for FedMeta Learning in Aerial Wireless Networks

Ruslan Zhagypar*, Nour Kouzayha, Hesham ElSawy, Hayssam Dahrouj, Tareq Y. Al-Naffouri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Federated Meta (FedMeta) learning integrates meta learning with Federated Learning (FL) towards addressing heterogeneity challenges in edge intelligence. Implementing FedMeta on uncrewed aerial vehicles (UAVs)-assisted aerial networks offers compound benefits, including optimal trajectory design and resource allocation. The quick adaptability of FedMeta to new edge devices with minimal data is challenged in adverse UAV-based wireless networks due to frequent update losses that lead to model bias and overfitting towards data from devices with better channels. In this letter, we propose an optimizer for the global FedMeta model update that is suitable for adverse channel conditions. We show that the proposed optimizer outperforms the state-of-the-art AdamW optimizer for FedMeta learning. By leveraging tools from stochastic geometry, particularly in UAV-orchestrated networks, we gain insights into channel behavior and integrate them into our algorithm. We also introduce a novel hybrid update rule combining our optimized strategy with AdamW, achieving superior convergence speed and overall accuracy. Extensive simulations on LEAF datasets under unreliable channel conditions validate the effectiveness of our methods.

Original languageEnglish (US)
Pages (from-to)646-650
Number of pages5
JournalIEEE Wireless Communications Letters
Volume14
Issue number3
DOIs
StatePublished - 2025

Bibliographical note

Publisher Copyright:
© 2024 IEEE. All rights reserved.

Keywords

  • FedMeta
  • Meta learning
  • stochastic geometry

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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