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    Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization

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    Name:
    aqfl.euromlsys21.pdf
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    1.587Mb
    Format:
    PDF
    Description:
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    Type
    Conference Paper
    Authors
    Abdelmoniem, Ahmed M.
    Canini, Marco cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2021-04-26
    Online Publication Date
    2021-04-26
    Print Publication Date
    2021-04-26
    Permanent link to this record
    http://hdl.handle.net/10754/669058
    
    Metadata
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    Abstract
    Federated learning (FL) is increasingly becoming the norm for training models over distributed and private datasets. Major service providers rely on FL to improve services such as text auto-completion, virtual keyboards, and item recommendations. Nonetheless, training models with FL in practice requires significant amount of time (days or even weeks) because FL tasks execute in highly heterogeneous environments where devices only have widespread yet limited computing capabilities and network connectivity conditions. In this paper, we focus on mitigating the extent of device heterogeneity, which is a main contributing factor to training time in FL. We propose AQFL, a simple and practical approach leveraging adaptive model quantization to homogenize the computing resources of the clients. We evaluate AQFL on five common FL benchmarks. The results show that, in heterogeneous settings, AQFL obtains nearly the same quality and fairness of the model trained in homogeneous settings.
    Citation
    Abdelmoniem, A. M., & Canini, M. (2021). Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization. Proceedings of the 1st Workshop on Machine Learning and Systems. doi:10.1145/3437984.3458839
    Publisher
    ACM
    Conference/Event name
    The 1st Workshop on Machine Learning and Systems (EuroMLSys)
    ISBN
    9781450382984
    DOI
    10.1145/3437984.3458839
    Additional Links
    https://dl.acm.org/doi/10.1145/3437984.3458839
    https://mcanini.github.io/papers/aqfl.euromlsys21.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1145/3437984.3458839
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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