## 3 System Model  
The system model considered in this paper is illustrated in Figure 2. In a time period (\(\mathcal{T}\)), the UAV has available battery power (\(\mathcal{P}_{b}\)), flying at a height (\(\mathcal{H}\)) in meters (m). The time period (\(\mathcal{T}\)) is divided into multiple transmission windows (\(L_{w}\)), where \(V\) vehicles sense and process sensor data and transmit the processed local models to the UAV.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Benefit** & **Short Description** \\ \hline Collaborative learning at the vehicles and the UAV & Co-operative learning enables collaboration between multiple vehicle clusters and edge servers for enhanced intelligence [61]. Furthermore, FRL can be utilized to optimize UAV trajectories and data transmission strategies, adapt UAV mobility, optimize computing resources, and make optimal scheduling decisions. \\ \hline Distributed learning at the vehicles and the UAV & The existing works have demonstrated that FL enhances intelligence and learning across distributed nodes and edge servers. FL has been used in jointly optimizing the UAV flying speed, flying locations, and bandwidth allocation for efficient data collection [62]. \\ \hline Decentralized learning at the vehicles and the UAV & In FL, the vehicles and the vehicle–UAV server pairs train their own models without a centralized aggregator. The vehicles usually collect sensing information, and the timely processing of the sensed information and transmission to the UAV using actor–critic DRL approaches minimizes the AoI by adapting the UAV scheduling strategies [63]. \\ \hline Data availability, reliability, and network scalability; energy-optimal computation offloading & FL allows to leverage the driving decisions obtained from massive amounts of vehicular data, without locally hosting the data [64]. Hence, using FL, new vehicles can easily be integrated into existing clusters to maximize the uplink and downlink capacity of all vehicles [65]. \\ \hline Fault recovery and minimal packet drops & In FL, the global model aggregation is based on local data from multiple vehicles in a cluster. Consequently, it is feasible to rapidly recover the losses due to vehicles experiencing connectivity issues and interference, or if a vehicle’s sensors malfunction. Further, multi-agent FL is used to minimize the UAV flight time by learning the UAV–vehicle association strategies [66]. \\ \hline Communication efficiency and shorter timeouts & Using FL, the model updates and hyper-parameters are communicated to the UAV server rather than gross data offloading. Using asynchronous FL, vehicles can be trained to drop redundant packets. Short-packet transmission can be harnessed to maximize computation efficiency, jointly optimize communication scheduling, and mitigate the impact of intermittent coverage [67]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Advantages of federated reinforcement learning in latency minimization and power optimization of UAV-assisted C-V2X networks.  
As per Equation (1), the data rate in bits/Hz transmitted by each vehicle over UAV flight time is computed based on the summation of the instantaneous transmission rate (\(s_{i,t}\)):  
\[s_{i,t}(\mathbf{\psi_{t}},\mathbf{w_{t}},\mathbf{\mathcal{D}}_{t})\!=\!\mathcal{B}_{j}\! \log_{2}\!\left(\!1\!+\!\frac{P_{i}^{t}|h_{i,j}^{t}|^{2}}{\sum\limits_{j=1}^{ \mathcal{T}_{-1}}P_{d_{j}}|h_{i,j}^{t}|^{2}\!+\!\mathcal{B}_{j}(\sigma_{ij}^{t })^{2}}\!\right) \tag{1}\]  
where \(\mathbf{\psi_{t}}\) is a binary variable, indicating whether a vehicle transmits local models to the UAV in a transmission window (\(L_{w}\)) or not, \(\mathbf{w_{t}}\) is the FL model weights at time \(t\), and \(\mathbf{\mathcal{D}}_{t}\) is the delay, which is the sum of the queuing delay and processing delay.