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Addressing Skewed Heterogeneity via Federated Prototype Rectification With Personalization.

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    Federated learning (FL) addresses data privacy by enabling collaborative training. This study introduces skewed heterogeneous FL (SHFL) and a new method, FedPRP, to improve model performance on imbalanced datasets.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Distributed Systems

    Background:

    • Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy.
    • A key challenge in FL is data-level heterogeneity, characterized by skewed or long-tailed data distributions across clients.
    • Existing FL methods often assume uniform data distribution, which is unrealistic in practical scenarios.

    Purpose of the Study:

    • To investigate federated learning under data-level heterogeneity, specifically in a more practical skewed heterogeneous FL (SHFL) setting.
    • To propose a novel approach, Federated Prototype Rectification with Personalization (FedPRP), to address the challenges of SHFL.
    • To enhance both personalization and generalization capabilities in federated learning models operating on skewed data.

    Main Methods:

    • The study redefines the problem setting as skewed heterogeneous FL (SHFL).
    • A novel method, FedPRP, is proposed, comprising two components: federated personalization and federated prototype rectification.
    • Federated personalization aims to create balanced decision boundaries for dominant and minority classes, while federated prototype rectification refines empirical prototypes using inter-class and intra-class information.

    Main Results:

    • Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed FedPRP approach.
    • FedPRP outperforms existing state-of-the-art methods in handling skewed data distributions in federated learning.
    • The method achieves a balanced performance between personalization and generalization.

    Conclusions:

    • The proposed FedPRP method effectively addresses the challenges of data-level heterogeneity in federated learning, particularly in the SHFL setting.
    • FedPRP offers a practical solution for improving model performance and fairness in real-world federated learning applications with imbalanced data.
    • This work advances the field of federated learning by providing a robust method for skewed data distributions.