Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. But in real distributed scenarios, Non-Independent and Identically Distributed(Non-IID) of data reduces the speed of learning and the accuracy of global model. To solve this problem, this paper proposes a federated learning client selection algorithm based on cluster label information(FedCLS). FedCLS realizes efficient federated learning by optimizing the selection of clients in each round of training. Through extensive simulations, we demonstrate that compared with traditional FedAVG based on random extraction, FedCLS has better learning performance and less resource overhead.


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    Titel :

    FedCLS:A federated learning client selection algorithm based on cluster label information


    Beteiligte:
    Li, Changsong (Autor:in) / Wu, Hao (Autor:in)


    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    292622 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch