# Embedded Federated Learning for VANET Environments
## 2 Related Work  
Federated learning (FL) is a recent paradigm in which machine learning (ML) processing is distributed, allowing several decentralized clients to cooperatively train a shared model globally [2]. Despite being recent, it is a topic of great interest, with a significant amount of work on distributed and federated learning [5; 6; 7; 8; 9]. Due to the scope of this work, we focused our assessment on the work related to the deployment of distributed/federated learning solutions on edge devices that are somehow used in intelligent transportation system (ITS) solutions within the scope of smart cities. [PERSON] et al. [1] proposed a vehicle detection algorithm based on the YOLOv3 model trained with a large volume of traffic data. This solution uses images to identify vehicles that generate a dataset used to train the model for flow detection. The model is trained on a centralized server and then migrated to the devices that use NVIDIA Jetson to perform the discovery work on edge devices. Despite using edge devices to carry out the identification, the entire training process is centralized. The work in [10] described a testbed assembled with Intel Movidius Neural Compute Stick and Raspberry Pi 3 Model B. The testbed hosts convolutional neural network (CNN) algorithms used to analyze objects in real-time videos for vehicular edge computing. The authors show a small performance evaluation of the algorithms in Raspberry-Pi-based devices. The authors of [11] proposed a communication-efficient FL framework, the goal of which is to improve FL convergence time. The authors used a probabilistic device selection scheme that aims to choose the most efficient ones in transmitting the model for aggregation. The authors formalized their schemes for probabilistic device selection and network resource management and analyzed their performance through simulation. Therefore, no evaluation was carried out with real devices. In [12], [PERSON] et al. presented a lightweight hybrid FL framework that uses blockchain smart contracts to add security to the edge training plane, manage the trust and authentication of the participating federated nodes, and distribute the trained models globally or locally based on the reputation of edge nodes and their loaded datasets or models. To evaluate the proposed solution, they used a hybrid testbed with various types of devices such as Jetson, Raspberry Pi, and others. The focus of this work and evaluation was the security added with the use of the blockchain-based solution, and therefore, they did not deepen their evaluation in other directions. [PERSON] et al. [13] presented an extension of Stack4 Things [14], a cloud platform, adapting its functionalities to edge devices in order to provide a federated learning (FL) platform. To evaluate the new platform and demonstrate the efficiency of the federated approach, a heterogeneous testbed was set up with different types of clients, a laptop, three Raspberry Pi 3s, and an NVIDIA Jetson Nano, distributed on a university campus so that they were not in the same network, thus emulating a distributed deployment. In this testbed, a smart city application was deployed that exploits deep learning techniques to classify the vehicles crossing the intersection for traffic analysis purposes. In this scenario, they measured the loss of the federated model, training time, accuracy, and acceleration; that is, they focused their evaluation on the performance of the federated option and did not evaluate scalability, consumption of edge devices, or network overhead. [PERSON] et al. [15] presented a study in which they compared the performance of single-board computers on NVIDIA Jetson Nano, NVIDIA Jetson TX2, and Raspberry Pi 4. In this comparative study, the authors used CNN algorithms applied to the datasets of fashion product images. In the benchmark work, they evaluated basic points such as memory and CPU consumption but did not evaluate scalability, the concurrent use of multiple instances, processing times, or the aspects related to communication.