Developing sustainable and environmentally friendly network AI solutions has attracted significant interest recently. Unfortunately, analyzing the overall environmental impact, the greenhouse emissions in particular, of a network AI implementation is known to be a notoriously challenging task. As a popular distributed AI framework, federated edge intelligence (FEI) has been promoted as a candidate technology for implementing network AI in 6G. Unfortunately, recent studies suggest that the FEI network may generate more carbon emissions than the traditional centralized AI solutions. In this paper, we propose a novel analytical framework to quantify and optimize the carbon emissions of FEI networks. We develop an analytical model for quantifying the overall carbon emissions required for constructing a shared model in FEI with the guaranteed accuracy level. We propose a server-dropping-based algorithm that removes the highest-emitting and low-contributing edge servers from participating in the model training to minimize the overall carbon emissions. We conduct extensive experiments and the experimental results show that our proposed algorithm reduces up to 80% of carbon emissions, compared to the traditional FL-based solution.


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    Titel :

    Towards Net-Zero Carbon Emissions in Federated Edge Intelligence


    Beteiligte:
    Cai, Haohui (Autor:in) / Xiao, Yong (Autor:in) / Li, Yingyu (Autor:in) / Zhou, Yujie (Autor:in) / Niyato, Dusit (Autor:in) / Sun, Sumei (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    515933 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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