# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 5 Experimental Setup  
The goal of the experiments and evaluation is to assess the accuracy and privacy performance of the proposed, differentially private, mobility-based federated learning model. For the experiments, we use digit images in the Federated Extended MNIST (FEMNIST) dataset that partitions the EMNIST dataset based on writers [66]. As such, the data replicate non-IID data distribution for federated learning. The mobility-based federated learning model (named _mobile FL_ in the resulting graphs) is implemented by using the tensorflow-federated and tensorflow-privacy libraries. For performance comparison, we also implement a conventional federated learning model (named _vanilla FL_), where a coordinator selects participants randomly.  
Experiments are run with 750 vehicles in total deployed in a one-square-kilometer area for one hour. Each vehicle is a candidate for a participant who executes a training process and is assumed to be connected to the coordinator at all times. The coordinator selects participating vehicles for each learning; it takes different selection strategies in different learning models and mobility patterns. Experiments are run in two different scenarios that differ in mobility models, mechanisms for data sensing, and differential privacy algorithms. In the first scenario with the RPGM model, each vehicle periodically detects its location and training data every 10 s for the mobile FL model. In the vanilla FL model, a coordinator clusters vehicles into groups based on their location and selects vehicles (as training nodes) from each group that detect the most recent training data. The second scenario with the RWP model sets a speed range of 5-20 m/s, a pause probability of 0.5, and a maximum pause duration of 5 min. Events are randomly generated following a Poisson distribution. An event can occur anywhere inside the simulation area, and once it occurs, it stays active for a limited amount of time. An active event is represented as a small area. Once a vehicle moves into the event area, it detects and records data samples related to the event. To account for the space-time locality, stored data are removed after a certain period of time after recording or when the vehicle drives away from the event area.  
Privacy performance is evaluated by a noise value in differential privacy. For experiments, the noise sensitivity of the training models (i.e., the mobile FL model and the vanilla FL model) must be determined by adaptive clipping. To this end, we conduct preliminary experiments after each model is multiplied by noise multiplier (\(z\)) values of 0, 0.5, and 0.75. The results identify a noise tolerance level that does not harm the accuracy performance critically at each model with a relatively small number of vehicles (10 vehicles). They can also provide the number of vehicles and noise multiplier values, which is necessary to ensure the accuracy of a model that an application wants, given target \(\delta\) and \(\varepsilon\) values. Next, the experiments run final trainings with these numbers. We note that a noise multiplier value of 0 represents that differential privacy is not applied to a training model, whereas values of 0.5 or 0.75 indicate privacy-enhanced training models in this work.