The advancement of pervasive systems has made distributed real-world data across multiple devices increasingly valuable for training machine learning models. Traditional centralized learning approaches face limitations such as data security concerns and computational constraints. Federated learning (FL) provides privacy benefits but is hindered by challenges like data heterogeneity (Non-IID distributions) and noise heterogeneity (mislabeling and inconsistencies in local datasets), which degrade model performance. This paper proposes a model-agnostic, quality-driven approach, called DQFed, for training machine learning models across distributed and diverse client datasets while preserving data privacy. The DQFed framework demonstrates improvements in accuracy and reliability over existing FL frameworks. By effectively addressing class imbalance and noise heterogeneity, DQFed offers a robust and versatile solution for federated learning applications in diverse fields. [ABSTRACT FROM AUTHOR]

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