Optimal Batch Allocation for Wireless Federated Learning
Jaeyoung Song Sang-Woon Jeon
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.