# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 1 Introduction  
The future smart transportation system is expected to see collaboration among intelligent autonomous driving systems, named the Internet of Vehicles, which will ultimately improve road safety [1]. While recent research has applied machine learning techniques to realize this vision, security issues still remain in the process of collecting data from each vehicle and transferring them to a central server. Federated learning has been proposed to address these issues by allowing each vehicle to perform model training locally and only sending updated model parameters to the server [2]. As an emerging technology, however, it still has various technical challenges that have not yet been solved [3]. For instance, inference attacks in joint learning on autonomous vehicles, where an attacker exploits a training model to infer information that should be unknown, may cause privacy problems [4; 5; 6]. Recent differential privacy may alleviate this problem; it is possible to protect personal information while maintaining data distribution characteristics by adding noise to the data [7]. However, when differential privacy is applied to model training, there is a trade-off between privacy preservation and model accuracy, showing lower accuracy than non-privacy models. The primary goal of this research is to measure and enumerate this trade-off in the Internet of Vehicles. While recent studies are actively exploring the performance issue of differentially private federated learning models in conventional network environments, there are few studies that address this topic in the Internet of Vehicles, especially taking into account mobility patterns. To clarify the accuracy of performance and protection level in the Internet of Vehicles, this paper proposes a method considering three factors: learning models, vehicle mobility,and a privacy algorithm. First, the method considers two learning models: a conventional learning model that is widely used in federated learning settings and a mobility-based federated learning model that uses vehicular knowledge generated according to vehicles' movement patterns. Next, it imports two mobility models: reference point group mobility and random waypoint. These models represent normal situations and road events like traffic accidents, respectively. Regarding the third factor, this paper proposes to adopt a local differential privacy with adaptive clipping method after considering the nature of the Internet of Vehicles. After applying our privacy algorithm to the learning models, experiments are run with various noise values, and the accuracy performance of the models is measured under different mobility scenarios. In non-accident traffic situations, the experimental results show that privacy-enhanced federated learning models degrade performance by 3.26% on average, which is acceptable; elevated levels of data protection could compensate for the loss. In the case of traffic events, two learning models show very different outputs. With the mobility-based model, the privacy enhancement does not harm performance much, with a 2.96% drop. With the conventional model, however, the degradation rate reaches 42.97% (from 73.17% to 41.73%). The main contributions of this paper are summarized as follows:  
* This work designs a method considering three factors--learning models, vehicle mobility, and a privacy algorithm--in order to evaluate the impact of mobility on differentially private federated learning. * This work modifies a differential privacy algorithm that takes the features of IoV into account. As a result, it works seamlessly on a mobility-based federated learning model. * The experimental results discover that a conventional learning model that is widely used in federated learning does not work well in a mobile, vehicular environment, especially when enhanced with a privacy method, and that an adaptive clipping method is more useful than prior methods because it guarantees model performance and the target degree of privacy preservation.