# Embedded Federated Learning for VANET Environments
## 4 Distributed Learning Laboratory Testbed
### ATCLL Overview and Its Datasets  
The datasets used in the evaluation were collected from the infrastructure in the city of Aveiro, the ATCLL. The ATCLL comprises a large number of Internet-of-Things (IoT) devices with communication, sensing, and computing capabilities constituting a smart city infrastructure [4]. The 44 communication points of access in the city are connected through fiber and millimeter-wave (mmWave) links and integrate radio terminals (WiFi, ITS-G5, C-V2X, 5G, and LoRa). All these points combine and interconnect a set of sensors, such as mobility sensors (radars, LiDARs, and video cameras) and environmental sensors. The RSUs, indicated on the map as smart lamp posts P1, P30, P33, and P35, combine communication and computing devices such as PCEngines APU2 units ([[https://www.pcengines.ch/apu2.htm](https://www.pcengines.ch/apu2.htm)]([https://www.pcengines.ch/apu2.htm](https://www.pcengines.ch/apu2.htm)), accessed on 22 November 2022) and NVIDIA Jetsons. APUs use AMD GX-412 TC with 4 CPU cores, 4 GB of DRAM, 256 GB of SSD, and Debian GNU/ Linux 11.  
As illustrated in the left side of Figure 3, network nodes are also installed in the vehicles and local boats to transmit the mobility and environment data to the data-processing center. In the vehicular network supported by the ATCLL, the vehicles are equipped with onboard units (OBUs) to establish a connection with the RSU and with the other vehicles, over ITS-G5 and 5G. One of the main objectives of the ATCLL is to provide a range of services that can make the city smarter by offering, for example, services to support the management of urban mobility or emergency and safety applications. To achieve this purpose, the ATCLL hosts a base software platform capable of hosting services at three levels of operation: on mobile devices (OBUs), on edge devices (RSUs), and on the central processing (cloud). Among the services hosted on mobile devices, it is possible to highlight the identification of the mobility of each vehicle (position, speed, etc.) and also the collection of sensing information, such as temperature, humidity, etc. In the edge devices, in addition to collecting and processing sensing information and performing data collection from mobile devices, the various types of activity identification stand out, such as the number and type of vehicles that pass through the coverage perimeter, the capture and evaluation of pictures, etc. Most of the information collected by mobile and edge devices is sent to a central unit, where it is analyzed to gain a strategic overview of the city \"behavior\".  
To perform the evaluation of the FedFramework deployed at edge devices, we opted for a simple system to predict the number of vehicles entering the city, and to do so, we  
Figure 3: ATCLL infrastructure.  
used the time series datasets from the radars. Each dataset contains the number of vehicles that entered the city, identified at the collection points (RSU/Client) installed in strategic locations in the city of Aveiro. More specifically, we selected a period of four weeks, from 22 April 2022 to 22 May 2022. In this period, we considered the vehicles that passed through RSU in P1, P30, P33, and P35, as shown in Figure 4. Vehicle accounting was closed hourly, generating 24 sets of accounts each day. During this period of four weeks, we used three weeks to train the models and used the trained models and compared their predictions with the data from the fourth week to identify the accuracy of the models.