Federated Learning (FL) is a new way of training models used in Internet of Things (IoT) systems. It is a method that maintains the privacy of client devices while improving model accuracy and reliability. However, there is a problem in FL applications due to the lack of incentives. Clients have different motivations and produce different quality datasets, which leads to a divergence in the quality of local models uploaded to the central server. To address this issue, we propose a new incentive model based on the Stackelberg game. The mechanism we suggest distributes rewards based on the quality of the models uploaded to the server by each client, rather than the amount of data trained. We transform the model into two optimization problems, and we propose a linear complexity algorithm to solve them. This algorithm can achieve the optimal solution and greatly reduce computational complexity, as shown in our experimental results.


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    Titel :

    Incentivizing Quality Contributions in Federated Learning: A Stackelberg Game Approach


    Beteiligte:
    Zhang, Weicong (Autor:in) / Wang, Qin (Autor:in) / Zhao, Haitao (Autor:in) / Xia, Wenchao (Autor:in) / Zhu, Hongbo (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    423708 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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