Distributed Quantized Transmission and Fusion for Federated Machine Learning

Omid Moghimi Kandelusy, Christopher G. Brinton, Taejoon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Federated machine learning (FL) is a powerful technology which can be implemented to exploit the sheer amount of geographically distributed data for enhanced computation. Exploiting the impending proliferation of wireless devices, in this paper, we incorporate distributed quantized transmissions for reliable connectivity to a remote FL server. We develop a novel theoretical framework for the convergence analysis of the proposed network under joint impact of communication bit error rate (BER), and model quantization, and participation control. We show that the convergence rate of the network is affected by the BER and it can be improved via participation control. Through simulation, we demonstrate that our proposed model can provide the same performance as the conventional FL networks based on point-to-point communication while the energy consumption is divided across the distributed nodes.

Original languageEnglish (US)
Title of host publication2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350329285
StatePublished - 2023
Externally publishedYes
Event98th IEEE Vehicular Technology Conference, VTC 2023-Fall - Hong Kong, China
Duration: Oct 10 2023Oct 13 2023

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference98th IEEE Vehicular Technology Conference, VTC 2023-Fall
CityHong Kong

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Convergence analysis
  • Distributed Systems
  • Distributed quantization and Fusion
  • Federated Machine Learning


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