Semi-Synchronous Federated Learning Protocol With Dynamic Aggregation in Internet of Vehicles

Abstract

In an Internet of Vehicle (IoV) system, federated learning (FL) is a new approach to process real-time vehicle data in a distributed way, which can improve the driving experience and service quality. However, due to the high mobility and uncertainty of vehicles, the existing federated learning protocols are difficult to meet the full requirements from an IoV system, such as efficient resource allocation, high precision learning, and fast convergence of learning algorithm. To solve the above problems, in this paper, we propose a semi-synchronous federated learning (Semi-SynFed) protocol, to improve the performance of machine learning at Internet of Vehicles. In Semi-SynFed, we first select appropriate nodes to participate the aggregation by its computing capacity, network capacity and learning value of training samples. Meanwhile, the dynamic waiting time technique is designed to adjust the server waiting time at each round dynamically, which makes federated learning process work much more efficient. Finally, a dynamic aggregation scheme is designed to aggregate model parameters in an asynchronous way. To evaluate the reliability of Semi-SynFed, we establish a simulation of IoVs based on the real world data. In the comparative experiments, the results show that the Semi-SynFed outperforms the existing federated learning protocols in terms of convergence speed and resource consumption.