There is still lacking a simple and comprehensive framework to model and optimize the overall energy consumption of an FEI network, especially in heterogeneous scenarios. This paper proposes a comprehensive framework to characterize the overall energy consumption of FEI networks. The computation and communication overhead as well as the number of coordination rounds required to train a satisfactory model are analytically modeled and evaluated. We investigate and compare the energy consumption of FEI networks with two popular distributed algorithmic implementations: FedAvg and FedMeta. We observe that although FedMeta consumes more energy than FedAvg in each single coordination round, the overall energy consumption of FedMeta is much lower than that of FedAvg. Finally, we evaluate the energy consumption of both algorithms based on a hardware prototype. Numerical results show that the overall energy consumption of FedMeta is 77.9% less than that of FedAvg.


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    Titel :

    Towards Energy Efficient Federated Meta-Learning in Edge Network


    Beteiligte:
    Li, Xubo (Autor:in) / Jia, Yuanjie (Autor:in) / Li, Yingyu (Autor:in) / Xiao, Yong (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    1103009 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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