<ul><li>This paper presents a novel federated learning solution, QHetFed, suitable for large-scale Internet of Things deployments.</li><li>QHetFed addresses the challenges of large geographic span, communication resource limitation, and data heterogeneity.</li><li>The approach is based on hierarchical federated learning over multiple device sets and integrates quantization and data heterogeneity into the learning process.</li><li>QHetFed consistently achieves high learning accuracy and outperforms other hierarchical algorithms in scenarios with heterogeneous data distributions.</li></ul>

A Hierarchical Federated Learning Approach for the Internet of Things

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