## 5 Federated Task Offloading in the Proposed Scheme  
The section discusses the data-intrinsic capabilities of the proposed scheme. Figure 5 presents the details. In this scheme, we consider that there are demarcated zones at \(E_{MC}\) that would functionally provide the availability of COVID-19 resources (beds, ventilators, and vaccines), which are provided by \(E_{HC}\). Based on this, at the communication layer, \(k\)\(E_{UAV}\) forms a swarm network, which is denoted as \(\{U_{1},U_{2},\ldots,U_{k}\}\). The swarm network is controlled through \(UAV_{c}\), designated as the controller node. The data captured by \(k\) UAVs in the network is shared to the \(UAV_{c}\), which offloads the same to nearby edge servers as tasks. In general, we assume that \(p\) tasks \(\{T_{1},T_{2},\ldots,T_{p}\}\) need to be offloaded to local proximity edge server \(E_{ser}\) in a zone \(Z\). A task-tuple between any \(k^{th}\) UAV controller to \(q^{th}\)\(E_{ser}\) is presented as follows. \[F_{t}=\{b_{k,q},c_{k,q},\Delta_{k,q}\} \tag{5}\]  
where \(b_{k,q}\) denotes the size of federated task \(F_{t}\) offloaded from \(k^{th}\) UAV swarm controller to the \(q^{th}\) edge node, \(c_{k,q}\), respectively, denotes the CPU cycle requirements by \(q^{th}E_{ser}\), and \(\Delta_{k,q}\) presents the task deadline information. However, please note that not all tasks are offloaded to \(E_{ser}\). A decision variable \(D(s)\) denotes which tasks can be solved at \(E_{UAV}\), which at \(UAV_{c}\), and only a subset \(t\) is offloaded to \(E_{ser}\). For example, the decision variable operates as follows. \[D(s)= \begin{cases}-1&b_{k}\leq MUL_{k}\ \&\&\ \ c_{k}<c(U_{k})\\ 0&b_{k}\leq M(UAV)\ \&\ \&\ c_{k}<c(UAV)\\ 1&b_{k}>M(UAV)\ \&\ \&\ c_{k}>c(UAV)\end{cases} \]  
where \(b_{k}\) denotes task size, which needs to be smaller than the available memory of \(U_{k}\) (local swarm drone), and CPU cycles in the range of \(U_{k}\). Similarly, the tasks might be operated at the controller node or the edge server node. \(E_{ser}\) might also require high-end processing, and thus is connected to the global cloud server (GAS) for execution. As per Shannon channel capacity, the maximum uploading time for \(E_{UAV}\) to delegate its task to \(E_{ser}\) is given as follows. \[T(F_{to})=B_{c}log_{2}(1+\frac{q_{k}g_{k}^{2}}{\omega_{k}B_{c}}) \tag{6}\]  
where \(T(F_{to})\) denotes the uploading time of federated task \(F_{to}\), \(B_{c}\) represents the channel bandwidth for the \(UAV_{c}\)-\(E_{ser}\) link, \(q_{k}\) denotes the transmission power of \(UAV_{k}\), \(\omega_{k}\) denotes the power density function, and \(g_{k}^{2}\) is the channel gain. A task \(T\) might be split for local execution (swarm network or controller) or might be remotely executed at \(E_{ser}\). In the case of local executions \(D(s)=\{-1,0\}\), the energy consumption and execution time are computed as follows. \[T_{t_{U_{k}}} =b_{k}/c(U_{k}) \tag{7}\] \[T_{t_{UAV_{k}}} =b_{k}/c(UAV_{c})\]  
Figure 5: Federated task offloading scheme for 3 FFs UAVs. \[E_{t_{U_{k}}} =\Xi_{m,n}b_{k} \tag{8}\] \[E_{t_{U,AV_{c}}} =\Xi_{m,n}b_{k}\]  
where \(T_{t_{U_{k}}}\), \(T_{t_{U,AV_{c}}}\), respectively, denote the execution time of \(U_{k}\) and \(UAV_{c}\), and \(E_{t_{U_{k}}}\) and \(E_{t_{U,AV_{c}}}\) denote the energy consumption for task execution. \(X_{im,n}\) denotes the coefficient of energy consumption by the \(m^{th}\) UAV controller when it connects with the \(n^{th}\)\(U_{n}\). In case of remote execution by \(E_{ser}\), where \(D(s)=1\), we add the link delay with \(T_{E_{ser}}\) and subtract the energy dissipation of the link. Mathematically, it is presented as follows. \[T_{E_{s}\sigma} =T_{l}+T_{E_{ser}} \tag{9}\] \[E_{E_{s}\sigma} =E_{E_{s}\sigma}-E(l)\]  
We consider the remote case, where FL learning is applied for route planning, parameter optimization, and optimization of network parameters. We consider that \(UAV_{c}\) sends an offloading request \(F_{lo}\)req to \(E_{ser}\).