Optimizing IoT Data Collection for Federated Learning Under Constraint of Wireless Bandwidth
Kengo Tajiri, Ryoichi Kawahara · 2024
The advent of the Internet of Things (IoT) has led to an exponential increase in data from devices usable for monitoring and managing various systems. Traditional central server data processing is becoming impractical due to the surge in IoT devices and data volume, resulting in high bandwidth and computational costs. In federated learning (FL), servers train models independently using their own data and transfer their models to a central aggregator where the models are aggregated. This process reduces the computational load and bandwidth usage inherent in centralized systems because data transfer can be reduced in FL. The study focuses on a scenario where base stations (BSs), each with a server, receive data from IoT devices, and BSs train models with their own data as participants of FL. Since the accuracy of the model is influenced by the data's amount and distribution across servers, which BS IoT devices transfer their data to is critical, while bandwidth limitations constrain that choice. The paper introduces a conditional optimization problem and solves the problem with a genetic algorithm to maximize FL model accuracy while adhering to bandwidth constraints. In the experiments, we compared the proposed optimization and naive method by numerical simulations and actual training deep learning models. As a result, the accuracies of all deep learning models improved when FL was performed based on the results obtained from our optimization compared to the naive method.