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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Affective Computing

    Background:

    • Federated learning (FL) addresses data privacy concerns in affective computing.
    • Conventional FL struggles with personalized emotion data variations across clients.
    • A privacy-utility paradox exists in current federated affective computing (FAC).

    Purpose of the Study:

    • To propose a framework that enhances federated affective computing (FAC).
    • To resolve the privacy-utility paradox in emotion recognition.
    • To improve the performance of FL in personalized emotion data scenarios.

    Main Methods:

    • Proposed an emotion hemisphere (EH) representation structure to unify global feature spaces.
    • Utilized emotional prior knowledge for feature space calibration.
    • Employed a normalized parameter importance matrix for guided model aggregation on the server side.

    Main Results:

    • The framework significantly improves performance in federated affective computing.
    • Demonstrated effectiveness and practicality through extensive experiments on three emotion datasets.
    • Alleviated slow convergence issues in global models caused by skewed label distributions.

    Conclusions:

    • The proposed framework effectively enhances federated affective computing by addressing data privacy and utility challenges.
    • The EH representation and guided aggregation improve model performance and convergence.
    • The approach offers a practical solution for privacy-preserving emotion recognition.