## 2 System Model  
Figure 2 illustrates the system model comprising the UAV-vehicle communication architecture that comprises \(\{\mathbf{V}_{1},\ldots,\mathbf{V}_{n}\}\) vehicles. The communication channel uses orthogonal time-frequency space (OTFS) modulation, which introduces frequency and spatial selectivity in the fading channel. It considers both LoS and NLoS components to estimate the channel characteristics. The UAV trajectory is a set of sequential coordinates denoted by \(\mathbf{\Lambda}_{(x,y)}\). The trajectory coordinates comprise and turning points, represented as \(\mathbf{\Lambda}_{(x,y)}\in[\mathbf{\Lambda}_{(0,0)},\mathbf{\Lambda}_{(0,1)} \ \ldots\ \mathbf{\Lambda}_{(1,0)},\mathbf{\Lambda}_{(1,1)}\ \ldots\ \mathbf{\Lambda}_{(m,m)}]\). The past UAV coordinates are stored and are used to predict the future UAV path. We model the changes in the UAV trajectory as incremental steps based on their velocity and directions. We assume a system with slot duration \(\tau\) that consists of a block-fading model. The downlink channel gain from the UAV (\(u\)) to a vehicle (\(v\)) is given by Equation (1). \[\bar{g}_{u\to v}^{(t)}=\left|h_{u\to v}^{(t)}\right|^{2}\sigma_{u\to v}^{(t)}, \quad t=1,2,\ldots,\tau \tag{1}\]  
where \(\sigma_{u\to v}^{(t)}\) represents the large-scale fading component that varies as vehicles change their position. The location of vehicle \(v\) at slot \(t\) is represented as \(\mathrm{x}_{v}^{(t)}\). The large-scale fading in \(dB\) is given by Equation (2). \[\sigma_{\mathrm{dB},u\to v}^{(t)}=\mathrm{PL}\Big{(}\mathrm{x}_{u},\mathrm{x}_ {v}^{(t)}\Big{)}+\vartheta_{u\to v}^{(t)} \tag{2}\]  
where \(PL\) is the path loss and follows log-normal shadowing from \(\mathrm{x}_{u}\) to \(\mathrm{x}_{v}^{(t)}\). The standard deviation in log-normal shadowing is denoted by \(\vartheta_{u\to v}^{(t)}\) that varies with the displacement of the vehicle during the last slot. The SINR \(\gamma_{v}^{(t)}\) at a vehicle at time slot \(t\) is defined as a function of the communication bandwidth \(\mathbf{b}_{w}^{(t)}\) and power \(\mathbf{p}_{w}^{(t)}\) given by Equation (3) as  
\[\gamma_{v}^{(t)}\Big{(}\mathbf{b}_{w}^{(t)},\mathbf{p}_{w}^{(t)}\Big{)}=\frac {\bar{g}_{k_{u}^{(t)}\to v}^{(t)}p_{v}^{(t)}}{\sum_{V}\bar{g}_{b_{u}^{(t)}\to v }p_{v}^{(t)}+\mathcal{N}^{2}} \tag{3}\]  
where \(\mathcal{N}^{2}\) is the power spectral density of additive white Gaussian noise. The downlink spectral efficiency \(C_{v}^{(t)}\) of vehicle \(v\) at time \(t\) is given by Equation (4). \[C_{v}^{(t)}=\log\Big{(}1+\gamma_{v}^{(t)}\Big{(}\mathbf{b}_{w}^{(t)},\mathbf{ p}_{w}^{(t)}\Big{)}\Big{)} \tag{4}\]  
Figure 2: System model: The vehicles sense environmental data and process them locally at the in-vehicle edge servers. The resulting local model hyper-parameters are communicated to the UAV. The UAV aggregates various local model hyper-parameters in a TTI and transmits the global model hyper-parameters to the vehicles. The channel coefficient is modeled as follows:  
\[h_{u,v}=\sqrt{\frac{\kappa_{u}}{\kappa_{u}+1}}\overline{h}_{u,v}+\sqrt{\frac{1}{ \kappa_{u}+1}}\widetilde{h}_{u,v} \tag{5}\]  
The symbols considered in this paper are described in Table 1.