Optimal Batch Allocation for Wireless Federated Learning
This work addresses efficiency in federated learning for wireless networks, offering incremental improvements in batch allocation to reduce training time.
The paper tackles the problem of minimizing completion time in wireless federated learning by analyzing iterations needed to reach a target performance and characterizing per-iteration time under TDMA and RA schemes. It proposes a step-wise batch allocation, shown to be optimal for TDMA and significantly reducing completion time for RA, with numerical experiments validating substantial reductions.
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.