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# Efficient Disaster-Resilient Network Slicing for URLLC in UAV-Enabled MEC Using Federated Learning

Ryma Boussaha, Yacine Challal

► To cite this version:

Ryma Boussaha, Yacine Challal. Efficient Disaster-Resilient Network Slicing for URLLC in UAV-Enabled MEC Using Federated Learning. 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET), Dec 2024, Doha, Qatar. pp.67-72, 10.1109/HONET63146.2024.10822992. hal-05008192

HAL Id: hal-05008192

https://hal.science/hal-05008192v1

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# Efficient Disaster-Resilient Network Slicing for URLLC in UAV-Enabled MEC using Federated Learning

Ryma BOUSSAHA* and Yacine CHALLAL§

*Laboratoire LMCS, Ecole nationale Supérieure d'Informatique, Algiers, Algeria

§University of Doha for Science and Technology (UDST), Doha, Qatar

Email: {r_boussaha}@esi.dz, yacine.challal@udst.edu.qa

**Abstract**—Upon natural or industrial disasters, the reliability and continuity of communications becomes an absolute priority to ensure the availability of critical services. In this context, network slicing emerges as a promising solution for isolating quality of service needs. In addition, URLLC (Ultra-Reliable Low Latency Communication) services are essential to enable effective coordination for real-time emergency management and remote equipment control. The integration of UAVs (Unmanned Aerial Vehicles) as flying MECs (Multi-access Edge Computing) to provide the essential resources for network slicing in disaster scenarios has a substantial effect on the scope of emergency operations management. UAVs involvement facilitates extending connectivity to remote areas, where mobile infrastructure is unavailable. However, due to inefficient node selection for resource allocation, the number of satisfied slicing requests may be reduced, which could put lives at risk in disaster-affected areas. The use of UAVs to ensure network slicing and energy transmission jointly poses a significant challenge given that the UAVs' energy is limited. In response to these challenges, we propose a new multi-objective optimization model enabling URLLC services. We also propose a proactive model that predicts the required workload in the network in terms of computing and storage capacity using federated learning. Our optimization model demonstrates a significant improvement compared to existing approaches, with a remarkable reduction in latency of 50%.

**Index Terms**—Disaster management, URLLC, UAV-assisted power transmission, flying MEC, federated learning.

## I. INTRODUCTION

Throughout disaster management operations, a range of services is necessary to address the diverse requirements of emergency situations. Network slicing allows to create isolated virtual network slices dedicated to each service, while ensuring real-time decision-making and improving performance requirements in terms of latency, bandwidth, reliability and availability. In this context, URLLC (Ultra-Reliable Low Latency Communications) services play a vital role in saving lives and minimizing damage. For example, as part of relief operations after an earthquake, URLLC services ensure effective coordination of rescue operations with permanent contacts even if the communication infrastructure is damaged. Furthermore, in the context of wildfire, URLLC services provide rescue and firefighting teams with real-time updates on the state of affected areas and the progress of the fire. The ultra-fast transmission of data collected by the sensors enables

to detect hot spots and predict the spread of fire to protect lives and properties in affected areas.

MECs (Multi-access Edge Computing) systems play a crucial role in optimizing and fully exploiting dynamic resource allocation for network slicing, by moving data processing and storage to the network edge. In addition, the deployment of MECs through UAVs (Unmanned Aerial Vehicles) enables to provide immediate communication and processing capabilities in the areas where they are most needed, while reducing rescue times. In addition to their use in deploying resources at the edge of the network, UAVs are very useful for wireless power transfer in disaster scenarios. Their ability to move and quickly access difficult areas to provide energy to multiple locations, will enable targeted energy distribution and ensure effective monitoring and management of available energy resources. However, UAVs have limited energy, so several parameters must be taken into consideration to provide better coverage of the drone network such as the choice of UAVs for energy transmission. Another issue in this scenario is keeping the exchanged data confidential, particularly in critical disaster scenarios such as in military zones. In fact, malicious and intrusive entities could exploit the exchanged data, even those necessary to ensure network slicing and the allocation of resources, with the intention of disrupting rescue operations.

To face the above-mentioned challenges, we designed a system that ensures network slicing for URLLC services. The allocation of resources is done at the edge of the network using flying UAV MECs, and we propose the following contributions:

1. We formulate a multi-objective optimization problem which aims to minimize the energy consumed by drones, to maximize the number of satisfied requests and to minimize latency to ensure an efficient URLLC service. This is carried out, while respecting the constraints relating to the resources available in each UAV (in terms of storage and calculation capacity and transmission power), and the required level of latency.

2. We leverage a federated learning based approach in order to preserve the confidentiality of network slicing related data in critical areas. Furthermore, to reduce response time and avoid delays caused by creating and configuring slices after requests are accepted, we
propose a proactive model, which predicts the load in the network in terms of calculation and storage capacities. This model takes into consideration the heterogeneity of the capabilities of the UAVs and the transmitted data, by applying a regularizer to the aggregated global model.

3) Our evaluation assessed the performance of federated learning algorithms in predicting workload dynamics and optimizing resource allocation accordingly.

This paper is organized as follows: Firstly, we present the Related Work, providing an overview of previous studies and related approaches. Secondly, the System Model elucidates the architecture of the edge network environment. Thirdly, the Problem Formulation introduces the optimization model for resource allocation. Fourthly, we delve into Federated Learning for Workload Prediction. Fifthly, the Evaluation Results present the outcomes of our simulation tests. Finally, the Conclusion section summarizes our findings and suggests directions for future research.

## II. RELATED WORK

Several recent studies have addressed various aspects of UAV-assisted communication and resource management in wireless networks. For instance, one study [1] focused on slicing the UAV network to accommodate different service requirements simultaneously, proposing a novel solution approach based on federated learning and a Lyapunov-based optimization methods. Another investigation [2] explored UAV-assisted edge computing systems and proposed a survivable resource slice embedding algorithm leveraging network slicing technologies. Additionally, in the context of disaster management, researchers in [3] have proposed optimal UAV system design and task assignment methodologies, along with computationally efficient solution approaches. Furthermore, studies in [4] have investigated the use of UAVs as flying mobile edge computing servers to provide computational and communication resources to low-power sensor nodes, employing decoupled optimization approaches for efficiency. Moreover, the integration of UAVs with wireless power transfer networks has been studied in [5], aiming to maximize ground user energy through deep reinforcement learning-based trajectory optimization approaches. In [6], the authors propose deploying multiple UAVs as aerial base stations in 3D space to maximize total user service time during disasters when terrestrial stations fail. The approach uses Voronoi Diagrams to divide the target area and optimally place UAVs to serve dynamic user distributions while meeting Quality-of-Service (QoS) requirements. Simulation results show that this method effectively maximizes service time and satisfies QoS for all users.

While these studies have made strides in UAV-assisted communication and resource management, challenges persist. These include the complexity of converging different types of services onto a common UAV network and the need for efficient resource allocation and management in disaster scenarios. Moreover, integrating UAVs with emerging technologies such as wireless power transfer networks and mobile edge computing presents further challenges in optimizing energy

<figure><img src="image_3.png" /><figcaption>Fig. 1. Network architecture</figcaption></figure>

efficiency and service quality. To address these challenges, our approach introduces a novel framework for network slicing in disaster scenarios, leveraging advanced optimization and machine learning techniques to dynamically allocate resources and prioritize communication services.

## III. SYSTEM MODEL

### A. Network architecture

Figure (1) illustrates the network architecture, which consists of three layers. The three layers are the "Air-Ground Interaction Layer (AGI) ", the "Radio Access Layer (RA) ", and the "Network Slicing Management Layer (NSM) ". The Air-Ground Interaction Layer manages and optimizes the exchange of data and control messages between UAVs and GmUs (ground mobile users). The main functionalities of this layer are: tracking UAVs in real time, planning the UAV trajectory, allocating aerial resources for network slicing, and optimizing channel gain between UAVs and GmUs.

According to the 3GPP management reference framework [7], the main features of the Radio Access Layer (RA) layer include cost-effective network infrastructure and the integrity and confidentiality of radio signaling and user plane messages. This layer allocates radio frequency spectrum, and ensures reliable communication between GmUs, UAVs and the core 5G network. The core component of the Radio Access Layer is the base station, which acts as a bridge between the AGI and the NSM layers. At the topmost level, the NSM Layer supports the configuration, creation, and activation of slices in order to meet the necessary requirements to execute specific services, such as low-latency communication, real-time streaming, and low-bandwidth massive data transmission.

### B. Network slicing

Depending on the nature of each task within the network and the system, we activate a slice. This is achieved by sending a different request for each type of slice. We consider URLLC, which offers low-latency communication for emergency response units. We note $S^u$ the set of URLLC slices, as the
slices are created on demand, the set $S^u$ is not fixed and can be updated when new slices are added.

The URLLC slice request is a tuple {$\mathcal{L}_s^u, \mathcal{R}_s^u, \mathcal{N}_s^u$}, where $\mathcal{L}_s^u$ represents the requested latency, $\mathcal{R}_s^u$ is the reliability level and it is determined by considering both the acceptable loss rate and the priorities of the tasks. $\mathcal{N}_s^u$ denotes the number of users requesting slice $s$, which can include UAVs or GmUs.

## IV. PROBLEM FORMULATION

Let's consider a disaster area where a fleet of UAVs play the role of Multi-access Edge Computing stations. Given the context of disaster relief, we consider specifically URLLC network slicing to ensure low latency and reliability for critical communications. We aim to minimize drones energy and to maximize the number of satisfied requests.

### A. Input parameters

The network is represented as a directed graph $G' = (V, E)$, such that $V$ is the set of nodes and $E$ is the set of edges between the nodes. The set $V$ is partitioned into two subsets, $V = U \cup G$. We assume that $U$ is the set of UAVs in the network, which acts as flying MEC providing at the same time storage and computation resources for network slicing, and wireless power transfer (WPT) to ground mobile users. The set $G$ represents the set of GmUs. Each UAV $u \in U$ is characterized by its position $(x_u, y_u, H)$, and each GmU $j \in G$ is characterized by its position $(x_j, y_j, 0)$. As mobile users are on the ground, we suppose that their altitude is equal to 0, whereas, the UAVs have all the same altitude, which is equal to H. We adopt a discrete time-space system, dividing the entire charging duration into $|T|^1$ discrete time slots, each with a duration of $\delta$. Table I summarizes the main sets, parameters, and decision variables used to form our model.

### B. Drones energy

The drone energy consumed during data transmission (Eq 1) depends on the transmission power $p_u^L(t)$ for Line of sight (LOS) and $p_u^N(t)$ for Non Line of sight (NLOS), as well as the amount of data transmitted and the transmission rate. We define the energy consumed by a drone $u \in U$ as follows:

$$E_u^r = \sum_{t \in \mathcal{T}} (p_u^L(t) + p_u^N(t)) \cdot \frac{M_u^*(t)}{\tilde{D}_u(t)} \quad (1)$$

Such that, the average transmission rate of each node $u \in U$ is calculated as the average of the transmission rate to all mobile users:

$$\tilde{D}_u(t) = \frac{1}{|U|} \sum_{j \in G} D_{u,j}(t) \quad (2)$$

The data rate can be estimated using Shannon Formula as follows:

$$D_{u,j}(t) = B_u \cdot \log_2 \left( 1 + \frac{\xi_{u,j}}{\sigma^2} \right)$$

<table><caption>TABLE I
INPUT PARAMETERS AND DECISION VARIABLES IN MIP</caption><thead><tr><th>Parameter</th><th>Meaning</th></tr></thead><tbody><tr><td>U</td><td>Set of unmanned aerial vehicles</td></tr><tr><td>G</td><td>Set of ground mobile users</td></tr><tr><td>S<sup>u</sup></td><td>Set of URLLC network slices</td></tr><tr><td>T</td><td>Set of time slots {1, 2, ..., |T|}</td></tr><tr><td>E<sub>u</sub><sup>*</sup>(t)</td><td>Energy of the UAV u ∈ U at time slot t ∈ T</td></tr><tr><td>E<sub>u,j</sub>(t)</td><td>Energy sent from the UAV u ∈ U to the GmU j ∈ G at time slot t ∈ T</td></tr><tr><td>p<sub>u</sub><sup>m</sup></td><td>Maximum power transmission of the UAV u ∈ U</td></tr><tr><td>E<sub>j</sub><sup>m</sup></td><td>Maximum energy of the GmU j ∈ G</td></tr><tr><td>B<sub>u</sub></td><td>Bandwidth of the UAV u ∈ U</td></tr><tr><td>M<sub>u</sub><sup>*</sup>(t)</td><td>Amount of data transmitted by the UAV u ∈ U at time slot t ∈ T</td></tr><tr><td>M<sub>j</sub>(t)</td><td>Amount of data transmitted by the GmU j ∈ G at time slot t ∈ T</td></tr><tr><td>D<sub>u,j</sub>(t)</td><td>Data rate between the UAV u ∈ U and the GmU j ∈ G at time slot t ∈ T</td></tr><tr><td>X<sub>s</sub><sup>s</sup></td><td>Amount of storage resources requested for the slice s ∈ S<sup>u</sup></td></tr><tr><td>X<sub>s</sub><sup>c</sup></td><td>Amount of computation resources requested for the slice s ∈ S<sup>u</sup></td></tr><tr><td>X<sub>u</sub><sup>m,s</sup></td><td>Maximum amount of storage resources available in the UAV u ∈ U</td></tr><tr><td>X<sub>u</sub><sup>m,c</sup></td><td>Maximum amount of computation resources available in the UAV u ∈ U</td></tr><tr><td>ε<sup>t</sup></td><td>Energy unit consumed during data transfer</td></tr><tr><td>ε<sup>d</sup></td><td>Energy unit consumed during movement.</td></tr><tr><td colspan="2"><strong>Decision variables</strong></td></tr><tr><td>χ<sub>s,u</sub>(t)</td><td>Amount of storage resources allocated for the slice s ∈ S<sup>u</sup> in the UAV u ∈ U at time slot t ∈ T</td></tr><tr><td>χ<sub>s,u</sub><sup>c</sup>(t)</td><td>Amount of computation resources allocated for the slice s ∈ S<sup>u</sup> in the UAV u ∈ U at time slot t ∈ T</td></tr><tr><td>δ<sub>s,u</sub><sup>s</sup></td><td>Binary variable indicating whether the UAV u ∈ U is used to allocate storage resources for the slice s ∈ S<sup>u</sup></td></tr><tr><td>δ<sub>s,u</sub><sup>c</sup></td><td>Binary variable indicating whether the UAV u ∈ U is used to allocate computation resources for the slice s ∈ S<sup>u</sup></td></tr><tr><td>δ<sub>u,j</sub><sup>p</sup></td><td>Binary variable indicating whether the UAV u ∈ U is used to transmit power to the GmU j ∈ G</td></tr></tbody></table>

$D_{u,j}(t)$ is the data rate in bits per second (bps). $B_u$ represents the bandwidth of the communication channel in Hertz (Hz). $\xi_{u,j}$ denotes the received power from UAV u to GMU j, measured in decibels-milliwatts (dBm). $\sigma^2$ represents the noise power, which is typically measured in watts.

### C. Amount of satisfied requests

In our model, requests are related to the resources allocation which can be either storage or computational resources.

**Proposition 1:** We assume that a request is satisfied if the amount of allocated resources is equal to the amount of requested resources. Moreover, by considering that a request

<footnote n="1">¹T denotes the set of time slots, $T = \{1, 2, ..., |T|\}$.</footnote>
can be satisfied by several drones, then the number of satisfied requests in the network at any time slot $t \in \mathcal{T}$ is given by:

$$ \mathcal{N}_u^a(t) = \frac{1}{2} \sum_{s \in S^u} \sum_{i \in \{s, c\}} \left( \lfloor 1 - \frac{X_s^i - \sum_{u \in U} (\chi_{s,u}(t) \delta_{s,u}^i(t))}{sup_s^i(t) - inf_s^i(t) + 1} \rfloor \right) \quad (3) $$

Such that, $\forall i \in \{s, c\}, \forall t \in \mathcal{T}, \forall s \in S^u$

$$ sup_s^i(t) = \max(X_s^i, \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))) \quad (4) $$

and,

$$ inf_s^i(t) = \min(X_s^i, \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))) \quad (5) $$

**Proof 1:** In our proposal, we assume that the amount of resources allocated does not exceed the requested one. Furthermore, knowing that $i$ can take the two values $s$ or $c$ which corresponds to storage and calculation, respectively, $s \in S^u$, and $t \in \mathcal{T}$, then we have,

$$ X_s^i \geq \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t)) \quad (6) $$

$$ \Rightarrow X_s^i - \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t)) \geq 0 \quad (7) $$

On the other side, we have

$$ X_s^i \leq \max(X_s^i, \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))) \quad (8) $$

and

$$ \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t)) \geq \min(X_s^i, \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))) \quad (9) $$

Using the two expressions of equations (8) and (9), we have

$$ X_s^i - \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t)) \leq sup_s^i - inf_s^i + 1 \quad (10) $$

$$ \Rightarrow 0 \leq \frac{X_s^i - \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))}{sup_s^i - inf_s^i + 1} \leq 1 \quad (11) $$

If the requested resources are not allocated completely then $X_s^i > \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))$, then

$$ \lfloor 1 - \frac{X_s^i - \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))}{sup_s^i - inf_s^i + 1} \rfloor = 0 \quad (12) $$

On the other side, if the requested resources are allocated completely then $X_s^i = \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))$, and

$$ \lfloor 1 - \frac{X_s^i - \sum_{u \in U} (\chi_{s,u}^i(t) \delta_{s,u}^i(t))}{sup_s^i - inf_s^i + 1} \rfloor = 1 \quad (13) $$

As a single query combines the storage and computing resources requirements, then the final cumulative result is divided by 2.

### D. URLLC slice MIP model

Our aim is to achieve an energy-efficient and ultimate flying MEC service by optimizing the storage and computation resource allocation $\chi(t)$ and the UAVs selection $\delta(t)$.

In summary, the slice embedding multi-objective optimization problem can be expressed as follows:

$$ \mathcal{F}_1: \text{Minimize} \quad \sum_{u \in U} E_u^* \quad (14) $$

$$ \mathcal{F}_2: \text{Maximize} \quad \sum_{u \in U} \sum_{t \in \mathcal{T}} N_u^a(t) \quad (15) $$

$$ \mathcal{F}_3: \text{Minimize} \quad \frac{1}{C} \sum_{t \in \mathcal{T}} \sum_{u \in U} \sum_{j \in G} \frac{M_u^*(t) + M_j(t)}{D_{u,j}(t)} \quad (16) $$

Subject to:

$$ \delta_{u,j}^p (p_u^L + p_u^N) < p_u^m, \quad \forall u \in U, \forall j \in G \quad (17) $$

$$ \sum_{s \in S} \chi_{s,u}^s(t) \delta_{s,u}^s(t) \leq X_u^{m,s}, \quad \forall u \in U, \forall t \in \mathcal{T} \quad (18) $$

$$ \sum_{s \in S} \chi_{s,u}^c(t) \delta_{s,u}^c(t) \leq X_u^{m,c}, \quad \forall u \in U, \forall t \in \mathcal{T} \quad (19) $$

$$ \delta_{s,u}(t) \sqrt{(x_u - x_j)^2 + (y_u - y_j)^2 + H^2} < R_u, \quad \forall u \in U, \forall s \in S, \forall j \in G \quad (20) $$

$$ \delta_{s,u}(t) = \min(\delta_{s,u}^c + \delta_{s,u}^s + \delta_{s,u}^p, 1) \quad \forall u \in U, \forall s \in S, \forall j \in G \quad (21) $$

$$ \frac{1}{|\mathcal{T}|} \sum_{t \in \mathcal{T}} \frac{M_u^*(t)}{D_{u,j}(t)} \leq \mathcal{L}_s, \quad \forall u \in U, \forall j \in G \quad (22) $$

Eqs. (14) and (15) aim to minimize the cumulative energy of the drones, and to maximize the total number of satisfied resource allocation requests. Besides, due to maximum capacity constraint, each UAV cannot exceed its maximum power transmission limit, Eq (17). Furthermore, it cannot surpass its upper storage or computation capacity, Eqs (18) and (19), respectively. To ensure that UAVs move in the same direction as the GmUs they serve, in terms of resource allocation or WPT (Wireless Power Transfer), then at time $t$ the distance between the UAV $u$ and the GmU $j$ must not exceed its radius $R_u$ representing its transmission range, Eqs (20), (21).

With URLLC type of slice, we add the goal of minimizing the average transmission latency, Eq (16), with respect to the constraint (22). Such that $C = |\mathcal{T} \times U \times G| = |\mathcal{T} \cdot U \cdot G|$. The latency of each UAV $u$ and each GmU $j$ must not exceed the latency required for any slice $s$.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Federated learning framework with centralized aggregation and agents distributed on UAV network</figcaption></figure>

## V. FEDERATED LEARNING FOR WORKLOAD PREDICTION

We employ federated learning for workload prediction in the network in terms of the required resources for storage and computation. Figure (2) illustrates the process of federated learning in our system which is based on a main centralized intelligent agent and local agents. We propose an approach which takes into consideration the heterogeneity of the nodes in the network. To achieve this goal, our solution is founded on the work already proposed in [8] and [9], where the authors proposed regularization and weighting mechanisms for data samples. In our system, we assume that the nodes, which represent the UAVs, are assigned ranks based on their respective losses. The formulas of both loss and rank are derived from the Welsch functions [10].

In the algorithm 1, during the first iteration ($t = 0$), the set of local agents is chosen according to the uniform probability distribution. Alternatively, if it's another round ($t \neq 0$), the BS first sorts all the UAV nodes according to their ranks, the nodes chosen are those which have the highest rank. After the selection of a UAV set $U_t$ the BS sends the global model to this set. Each UAV uses this global model and optimizes its local parameters to minimize the new loss function $\mathcal{L}_k^t$. The loss function also takes into consideration a regularizer $\frac{\mu}{2}\|\omega - \omega^t\|^2$ which depends on the parameter $\mu$ indicating the targeted level of regularization. Upon the computation of local model $\omega^{t+1}$, each node calculates the new rank value and sends its rank and the new model $\omega^{t+1}$ to the base station. The base station carries out a weighted aggregation of the received local models. The weight of each node depends on its rank $\tilde{r}_k$. The local intelligent agent deployed in each UAV uses the LSTM learning model.

## VI. EVALUATION RESULTS

In this study, We implemented our approaches using Python. We developed a framework to assess key performance metrics, including loss, drone energy consumption, and latency. Our evaluations primarily revolved around comparing our proposed

**Algorithm 1** HARP: Heterogeneity-aware with ranks and proximal updates for federated learning

**Input:** $T, N, \mu, \lambda, \omega^0$

**Output:** $\omega^{t+1}$

1. **for** $t = 0$ **to** $T-1$ **do**

2. **if** ($t==0$) **then**

3. The server selects a set of $N$ UAVs. Each UAV is chosen according to uniform probability distribution.

4. **else**

5. The UAVs are sorted according to their ranks $r_k$.

6. The server selects a subset $U_t$ of $N$ UAVs with the highest ranks.

7. **end if**

8. The server sends $\omega^t$ to all chosen UAVs. $k = 1, 2, \dots, N$

9. **for** $k = 1$ **to** $N$ **do**

10. Each UAV $k$ finds a $\omega_k^{t+1}$, a $\gamma_k^t$-inexact minimizer of $w_k^{t+1} \approx \operatorname*{arg\,min}_w [\lambda^2(1 - e^{-\frac{t(y_k^t, f(\omega^t, x_k^t))}{\lambda^2}}) + \frac{\mu}{2}\|\omega - \omega^t\|^2]$

11. The UAV $k$ computes the rank $r_k = e^{-\frac{t(y_k^t, f(\omega^t, x_k^t))}{\lambda^2}}$

12. The UAV $k$ sends $(\omega_k^{t+1}, r_k)$ to the BS

13. **EndFor**

14. The base station aggregates local models and updates the global model. $\omega^{t+1} = \frac{1}{N} \sum_{k \in U_t} \tilde{r}_k \omega_k^{t+1}$

15. **EndFor**

approach with FedAvg [11] in the context of federated learning.

### A. Federated Learning Analysis

We conducted a comparative study between our proposed approach, denoted as HARP (with regularization parameter $\mu$), and the conventional Federated Averaging (FedAvg) algorithm. Table II presents the results of this analysis. The columns in the table represent different values of the regularization parameter $\mu$, ranging from 0.2 to 0.8 for the HARP approach, as well as the FedAvg algorithm. We report the loss values for three different types of models: Regression (REG), Long Short-Term Memory (LSTM), and Neural Network (NN). Our results indicate that smaller values of $\mu$ tend to result in smaller loss values, indicating a stronger regularization effect. Conversely, larger values of $\mu$ lead to higher loss values, suggesting weaker regularization. Furthermore, we observe that the performance of the HARP approach varies with the number of participating agents. In most cases, increasing the number of agents results in lower loss values, indicating improved model convergence. Comparing the performance of the HARP approach with FedAvg, we observe that HARP achieves competitive or superior results in terms of loss reduction across different values of $\mu$ and numbers of agents.

### B. Multi-objective Optimization Model Assessment

We conduct a comparative analysis between our proposed latency optimization technique, referred to as OPT, and a technique that selects the drone with the maximum capacity
<table><caption>TABLE II
COMPARATIVE STUDY OF HARP MODEL WITH FEDAVG: IMPACT OF REGULARIZATION PARAMETER AND NUMBER OF AGENTS ON LOSS</caption><thead><tr><th rowspan="2">Number of agents</th><th colspan="3">μ = 0.2</th><th colspan="3">μ = 0.5</th><th colspan="3">μ = 0.8</th><th colspan="3">FedAvg</th></tr><tr><th>REG</th><th>LSTM</th><th>NN</th><th>REG</th><th>LSTM</th><th>NN</th><th>REG</th><th>LSTM</th><th>NN</th><th>REG</th><th>LSTM</th><th>NN</th></tr></thead><tbody><tr><td>4</td><td>0.276</td><td>0.755</td><td>0.283</td><td>0.253</td><td>2.852</td><td>0.338</td><td>0.295</td><td>4.048</td><td>0.441</td><td>8.393</td><td>7.087</td><td>8.086</td></tr><tr><td>8</td><td>0.281</td><td>0.598</td><td>0.265</td><td>0.302</td><td>1.706</td><td>0.297</td><td>0.358</td><td>3.395</td><td>0.507</td><td>8.743</td><td>7.847</td><td>7.442</td></tr><tr><td>16</td><td>0.255</td><td>0.890</td><td>0.280</td><td>0.274</td><td>1.741</td><td>0.300</td><td>0.287</td><td>2.111</td><td>0.345</td><td>7.442</td><td>9.130</td><td>7.715</td></tr><tr><td>32</td><td>0.245</td><td>0.604</td><td>0.264</td><td>0.270</td><td>1.076</td><td>0.283</td><td>0.270</td><td>1.177</td><td>0.323</td><td>11.238</td><td>10.441</td><td>8.143</td></tr><tr><td>64</td><td>0.252</td><td>0.387</td><td>0.255</td><td>0.253</td><td>0.620</td><td>0.273</td><td>0.262</td><td>0.883</td><td>0.284</td><td>12.028</td><td>11.382</td><td>8.867</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 3. Comparative study of Drone Energy Consumption</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Comparative study of Latency</figcaption></figure>

(MAX). Figures (3) and (4) presents the results, where the x-axis denotes the number of drones involved in the optimization process, and the y-axis represents energy consumption in Figure (3) and latency in Figure (4). We discern significant differences as the number of drones increases. In scenarios with smaller slice sizes ($s = 4$ and $s = 8$), optimal latency and energy values fluctuate, reflecting variations in the efficiency of our optimization strategy, whereas in the MAX strategy, values decrease steadily with an increasing number of drones. Conversely, larger slice sizes ($s = 16$) result in higher optimal values of energy and latency across all drone counts, demonstrating the influence of slice size on latency optimization.

## VII. CONCLUSION

Throughout this study, we have developed and evaluated a framework for network slicing in disaster scenarios to ensure URLLC services. Leveraging advanced federated learning algorithm, and UAV-based infrastructure, our slicing system was designed to adaptively allocate resources and prioritize communication services during critical events. Through various simulation tests and analysis, we have demonstrated the

effectiveness of our model in improving latency compared to baseline approaches. Our study suggests promising directions for future research. Specifically, evaluating the feasibility and costs of implementing network slicing for URLLC services with UAVs, including the integration of local intelligent agents, offers valuable insights. Additionally, identifying the optimal number of drones needed to ensure adequate coverage, network resilience, and continuous communication under realistic conditions will enhance the robustness of the proposed solution.

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