Optimal Batch Allocation for Wireless Federated Learning
<|reference_start|>Optimal Batch Allocation for Wireless Federated Learning: Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: time-division multiple access (TDMA) and random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the non-zero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction.<|reference_end|>
@article{song2024optimal,
    title={Optimal Batch Allocation for Wireless Federated Learning},
    author={Jaeyoung Song, Sang-Woon Jeon},
    journal={arXiv preprint arXiv:2404.02395},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02395},
    primaryClass={cs.LG cs.DC}
}