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Federated Aggregation With Interlayer Personalized Contribution: Preference-Based Optimization Between Performance

Xiaoting Sun, Zhong Li, Changjun Jiang

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    Summary
    This summary is machine-generated.

    This study introduces FedIPC and FedAPI-NSGA-II for personalized federated learning (PFL). These methods improve model aggregation by considering internal layer contributions and user preferences for better performance and privacy balance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Distributed Systems

    Background:

    • Personalized federated learning (PFL) addresses data heterogeneity across users.
    • Existing PFL methods neglect inter-layer contributions during aggregation.
    • Balancing user privacy and model performance with qualitative preferences is challenging.

    Purpose of the Study:

    • To propose a federated aggregation framework (FedIPC) considering inter-layer contributions.
    • To develop a multi-objective optimization method (FedAPI-NSGA-II) for adaptive preference matching.
    • To enhance PFL by improving model performance and aligning with user preferences.

    Main Methods:

    • Federated Aggregation with Inter-layer Personalized Contribution (FedIPC) framework.
    • Multi-objective optimization using adaptive preference indicators and NSGA-II.
    • Extensive experiments on image and tabular datasets.

    Main Results:

    • Accelerated model convergence in federated learning.
    • Significant improvements in overall model performance.
    • Effective matching of user preferences for privacy-performance trade-offs.

    Conclusions:

    • FedIPC enhances PFL by incorporating inter-layer contributions.
    • FedAPI-NSGA-II successfully balances model performance and user-defined privacy preferences.
    • The proposed methods offer a robust solution for personalized federated learning.