## 2 Background
### An Overview of Federated Learning
#### 2.2.1 Definition of Federated Learning  
FL facilitates the collaborative training of a machine learning model by many parties without the need for the direct exchange of their respective local data. The subject matter encompasses a range of methodologies derived from various fields of research, including distributed systems, machine learning, and privacy. Building on the definitions of FL provided by previous studies [19; 21; 22] we propose the following definition for FL. In an FL framework, numerous entities work together to train machine learning models without  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Sensor** & **Use of Sensors in Vehicles** \\ \hline \multirow{4}{*}{Camera} & Identifies traffic signs, enhances night vision, adapts to the light system, determines the likelihood of being involved in a collision, detects lanes, records emergencies, and provides parking aid. \\ \hline \multirow{4}{*}{GPS} & Tracks location, provides path direction, minimizes fuel costs, lowers operational costs, helps with theft recovery and in an emergency. \\ \hline \multirow{4}{*}{Ultrasonic sensors} & Include parking assist systems, which monitor the immediate surroundings of the vehicle and measure distance to obstacles. \\ \cline{2-3}  & Ensures safe navigation by detecting objects and estimating distances. \\ \hline \multirow{4}{*}{Radar} & Detects obstacles or pedestrians, deploys automatic emergency braking, and enables blind-spot monitoring, lane-keeping assistance, and parking assistance in autonomous mode. \\ \hline \multirow{4}{*}{Inertial sensors} & Provide data concerning the rate of acceleration and the current direction of the vehicle, includes automotive safety systems like airbag and anti-skidding protection. \\ \cline{2-3}  & Monitors tire air pressure and alerts the driver when it falls dangerously low. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common sensors in vehicle sensor networks.  
the need to share their raw data. The result of the process is a machine learning model for each entity involved (which may be identical or distinct). A crucial restriction of a practical FLS is that the performance of the model acquired through FL should surpass that of a model obtained through local training when evaluated using a designated measure, such as test accuracy, using the same model architecture. FLSs include the following aspects:  
* Data privacy: An FLS tackles the issue of data privacy by enabling individual entities to maintain their data locally, hence avoiding the need to share it with a centralized server. This is especially crucial when handling private or sensitive data [23].
* Collaborative training: Models are trained collaboratively within the FLS. Based on its local data, each party or device independently computes updates to the model and shares them with other participants or a central server.
* Aggregated model: By combining the model updates from each participant, the central server creates an enhanced global model that gains from everyone's combined expertise. The participants then receive a copy of this combined model.
* Iterative process: The iterative nature of the FLS entails the incorporation of several training rounds. During each iteration, individuals involved in the process update their respective local models and then contribute to the overall global model. The aforementioned iterative procedure persists until the global model reaches a satisfactory performance level.
* Customized models: The FLS enables the customization of models to cater to each participant's specific needs and requirements. Participants may have models customized to their individual needs, depending on the distribution of data and local requirements.