Energy Efficient Collaborative Federated Learning Design: A Graph Neural Network based Approach

Nuocheng Yang, Sihua Wang, Mingzhe Chen, Christopher G. Brinton, Changchuan Yin

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


In this paper, we consider the design of an energy efficient collaborative federated learning (CFL) methodology where devices exchange their local FL parameters with a subset of their neighbors without reliance on a parameter server. In the considered model, mobile devices implement the designed CFL to train their local FL models using their own datasets over a realistic wireless network. Due to the limited wireless resources and user movements, each device may not be able to transmit its FL parameters with all neighboring devices. Therefore, each device must select a subset of devices to share its FL parameters and optimize the transmit power. This problem is formulated as an optimization problem, whose goal is to minimize CFL training energy consumption while satisfying the delay and CFL training loss requirements. To solve this problem, a two-stage solution is proposed. At the first stage, a graph neural network (GNN) based algorithm is proposed, which enables each device to individually determine the subset of devices to transmit FL parameters using its neighboring devices' location and connection information. Compared to standard iterative algorithms that need to iteratively optimize device connections and transmit power, the proposed GNN based method can directly obtain the optimal device connections without iterative optimization. Given the optimal device connections, at the second stage, each device can directly obtain the optimal transmit power. Simulation results show that the proposed algorithm can decrease energy consumption by up to 46% compared to the algorithm where each device will directly connect to its first and second nearest neighbors.

Original languageEnglish (US)
Title of host publicationGLOBECOM 2023 - 2023 IEEE Global Communications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350310900
StatePublished - 2023
Event2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia
Duration: Dec 4 2023Dec 8 2023

Publication series

NameProceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813


Conference2023 IEEE Global Communications Conference, GLOBECOM 2023
CityKuala Lumpur

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


  • Collaborative federated learning
  • energy consumption optimization
  • graph neural network


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