Optimizing Data Distribution for Federated Learning Under Bandwidth Constraint
Kengo Tajiri, Ryoichi Kawahara · 2023
Federated learning, which can distributedly and efficiently train a deep learning model, attracts much attention since a large amount of data generated on a wide network can be utilized. When data generated in a network are collected, how much data is collected on which servers affects the model training time. However, the bandwidths of the links constrain the amount of data collected at each server and transfer routes of data. In this paper, we propose the optimization formula for data destinations and transfer routes in each generating data site under the constraint of the bandwidth of links in a network to minimize the time consumed by training a model. In the experiments, first, we numerically compared the amount of data collected in each server between the proposed algorithm and a naive method. The experiment shows that the proposed method could conduct the federated learning using all data generated in a network even when the bandwidths of links were less than one-third compared to the naive method. Then, we compared the training time of a deep learning model on the basis of the amount of data calculated in the former numerical experiments. We exhibit that the proposed algorithm reduced the training time by 27% to 47% compared to the naive method.