Improving Accuracy and Convergence in Group-Based Federated Learning on Non-IID Data
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DOI: 10.1109/tnse.2022.3163279
Abstract: Federated learning (FL) enables a large number of edge devices to learn a shared model without data sharing collaboratively. However, the imbalanced data distribution among users poses challenges to the convergence performance of FL. Group-based… read more here.
Keywords: federated learning; group; accuracy convergence; auto group ... See more keywords