Wireless federated learning can cut end-to-end training time by choosing per-device batch sizes with a closed-form rule that balances convergence rounds against per-round latency.
A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,
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Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity
Wireless federated learning can cut end-to-end training time by choosing per-device batch sizes with a closed-form rule that balances convergence rounds against per-round latency.