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Collaborative Machine Learning: Schemes, Robustness, and Privacy.

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    Summary
    This summary is machine-generated.

    Distributed machine learning (ML) enables efficient computation and in-situ learning while preserving data privacy. However, this approach faces significant vulnerabilities regarding data privacy and model robustness against malicious data.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Distributed machine learning (ML) was developed for efficient computation resource utilization.
    • ML is now extended for in-situ learning and private model sharing.

    Purpose of the Study:

    • To provide a comprehensive survey of privacy, security, and robustness issues in distributed ML.
    • To highlight vulnerabilities arising from extended objectives of distributed learning.

    Main Methods:

    • Literature review of existing research on distributed ML vulnerabilities.
    • Analysis of privacy concerns related to training data.
    • Examination of robustness issues caused by adversarial or erroneous training data.

    Main Results:

    • Distributed ML presents significant privacy risks for training datasets.
    • Model robustness is compromised by malicious or corrupted training data.
    • Security vulnerabilities are a growing concern in distributed learning environments.

    Conclusions:

    • Addressing privacy, security, and robustness is crucial for the safe adoption of distributed ML.
    • Further research is needed to develop robust and privacy-preserving distributed ML techniques.