Optimal Batch Allocation for Wireless Federated Learning

doi:10.1109/JIOT.2024.3516123

Optimal Batch Allocation for Wireless Federated Learning

2024-01-01

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DOI：10.1109/JIOT.2024.3516123

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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: 1) time-division multiple access (TDMA) and 2) 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 nonzero 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.

Keyword:

Computational modeling

Servers

Data models

Resource management

Internet of Things

Adaptation models

Time division multiple access

Optimization

Wireless communication

Batch allocation

federated learning

multiple access

wireless distributed learning

Optimal Batch Allocation for Wireless Federated Learning

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