## 3 Use of DT and FL
### IoD  
The IoD is a network of drones. The use of FL, which is also known as collaborative learning, has proven to be very effective in the deployment of IoD. ML is used to supply numerous services in fog-affected IoDs, where vast amounts of training data are gathered \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** & **Objective** & **FL** & **DT** & **Achievements** \\ \hline
[64] & 2021 & Traffic data predication & No & Yes &
\begin{tabular}{c} DT to predict the traffic data of the IoV and achieved quick response and improved accuracy \\ \end{tabular} \\
[65] & 2021 & Resource allocation & No & Yes & DT for the dynamic resource allocation in the aerial-assisted IoV \\
[66] & 2019 & Time series behavior modeling & No & Yes & DT model for the evaluation and validation of charging recommendation design \\
[67] & 2021 & Resource sharing & No & Yes &
\begin{tabular}{c} DT for the remote resource sharing in IoV and applied consortium blockchain to track and secure resource sharing \\ \end{tabular} \\
[68] & 2021 & Resource allocation & No & Yes &
\begin{tabular}{c} DT for the resource allocation in air \\ assisted-IoV and achieved improved energy efficiency \\ \end{tabular} \\
[69] & 2020 & Data sharing & Yes & No &
\begin{tabular}{c} Asynchronous FL-based approach for \\ efficient and secure data sharing \\ \end{tabular} \\
[70] & 2020 & TCP performance improvement & Yes & No &
\begin{tabular}{c} FL-based model for the improved TCP \\ performance over WiFi in an IoV network \\ \end{tabular} \\
[71] & 2020 & Knowledge sharing & Yes & No &
\begin{tabular}{c} Improved knowledge sharing in the \\ IoV-based blockchain-empowered \\ FL algorithm \\ \end{tabular} \\
[72] & 2021 & License plate recognition & Yes & No &
\begin{tabular}{c} Improved license plate recognition system \\ based on FL for the 5G-enabled IoV \\ \end{tabular} \\
[73] & 2021 & Accurate learning and privacy preservation & Yes & No &
\begin{tabular}{c} Dual-layer FL and heterogeneous model \\ aggregation for the efficient communication and privacy preservation of 6G-enabled IoV \\ \end{tabular} \\
[74] & 2021 & Cooperative positioning & Yes & No &
\begin{tabular}{c} FL-based cooperative positioning in an IoV network for autonomous driving and collision avoidance \\ \end{tabular} \\
[75] & 2021 & Joint resource management & Yes & No &
\begin{tabular}{c} Efficient resource management based on the \\ FL in an IoV network \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the use of DT and FL in IoV. by drones and evaluated in the nodes experiencing fog. Aggregating all data in these nodes may result in massive network traffic and data privacy leaks from drones. FL is therefore suggested to protect drone data privacy by performing local training in drones and communicating training model parameters to the fog-bound nodes without uploading raw drone data. However, ground eavesdroppers can still access private drones by eavesdropping and analyzing uploaded parameters during the FL training process. [PERSON] et al. examined the power regulation of all drones to optimize the FL system security rate while considering drone battery capacities and quality of service (QoS) requirements (i.e., FL training time) [76]. Smart entities, such as smart automobiles having the ability to detect and act, are common in the modern day. During their operational period, these entities can collect a large amount of data, which can then be used to improve the well-being of inhabitants. However, these data are extremely sensitive, prompting concerns about privacy. Furthermore, network constraints, bandwidth use, and other factors may exacerbate the situation. FL, IoD, and dew computing (DC) are transformative technologies that may be used to address the aforementioned issues. Using IoD, a FL-based computing paradigm was implemented over DC to analyze road-related data in order to improve the efficiency of applications, such as for identifying parking places [77]. Similarly, [PERSON] et al.