# Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated
Learning

Youssra Cheriguene, Wael Jaafar, Chaker Abdelaziz Kerrache, Halim
Yanikomeroglu, Fatima Zohra Bousbaa,
and Nasreddine Lagraa Y. Cheriguene, is with Université Amar Telidji de
Laghouat, Laghouat, Algeria, and a visiting scholar at Carleton University,
Ottawa ON, Canada (e-mail: y.cheriguene@lagh-univ.dz). W. Jaafar is with the
École de Technologie Supérieure, Montreal, QC, Canada (e-mail:
wael.jaafar@etsmtl.ca). H. Yanikomeroglu is with Carleton University, Ottawa,
ON, Canada (e-mail: halim@sce.carleton.ca). C.A. Kerrache, F.Z. Bousbaa and N.
Lagraa are with Université Amar Telidji de Laghouat, Laghouat, Algeria
(e-mails: {ch.kerrache, f.bousbaa, n.lagraa}@lagh-univ.dz).

###### Abstract

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked
a rise in research interest as a result of the massive and heterogeneous data
collected by UAVs, as well as the privacy concerns related to UAV data
transmissions to edge servers. However, due to the redundancy of UAV collected
data, e.g., imaging data, and non-rigorous FL participant selection, the
convergence time of the FL learning process and bias of the FL model may
increase. Consequently, we investigate in this paper the problem of selecting
UAV participants for edge FL, aiming to improve the FL model’s accuracy, under
UAV constraints of energy consumption, communication quality, and local
datasets’ heterogeneity. We propose a novel UAV participant selection scheme,
called data-efficient energy-aware participant selection strategy (DEEPS),
which consists of selecting the best FL participant in each sub-region based
on the structural similarity index measure (SSIM) average score of its local
dataset and its power consumption profile. Through experiments, we demonstrate
that the proposed selection scheme is superior to the benchmark random
selection method, in terms of model accuracy, training time, and UAV energy
consumption.

###### Index Terms:

Unmanned aerial vehicle, UAV, federated learning, FL, edge computing.

##  I Introduction

In 5G and beyond communication networks, edge computing is considered as one
of the enabling technologies for low-latency and mission-critical applications
[1, 2]. In contrast to edge servers at fixed locations, flying unmanned aerial
vehicles (UAVs)-enabled mobile edge computing (MEC) plays a vital role in
providing wide-area flexible deployment [3, 4, 5], reliable communication [6,
7, 8], data collection [9, 10], and mobile MEC services [11, 12]. Due to their
imagery capabilities, UAVs’ collected imaging data can be exploited for a
plethora of applications, such as traffic monitoring [13, 14], road extraction
[15], and remote sensing [16]. These applications need fast and real-time data
analysis, thus requiring to build customized and secure machine learning (ML)
models.

Traditional ML models, which are based on the aggregation of the UAVs’ sensing
data at a central entity, e.g., an edge server, may raise serious privacy and
data misuse concerns, due to the broadcast nature of wireless communications
and the vulnerability of the central server. Moreover, transmitting raw data
streams, such as UAV images/videos, to the edge server, consumes a lot of the
wireless bandwidth and may saturate it at peak times. Also, such behavior may
rapidly drain the UAV’s battery [17].

To tackle this issue, federated learning has been recently proposed as a
promising distributed ML paradigm [18]. FL allows a group of participants to
collaboratively train their ML models without disclosing their local data
within their communications with the central server, whereas the latter
aggregates the received non-critical data to train a global model and send
updates to the FL participants. Typically, not all available devices
participate in the FL process. Indeed, only a subset is selected each time to
participate in FL [18]. This approach allows handling heterogeneous devices
with different ML capabilities and data properties. Although random
participant selection is a common strategy, it leads often to weak FL
performances.

In addition, in the context of UAV systems, redundancy in collected data
occurs often and in different ways, such as exact and near-duplicate images
acquired by different UAVs. Redundancy has a negative impact as it causes an
unnecessary usage of storage space, computation resources, and power.
Moreover, it may lead to biased Ml models due to the lack of new and
diversified information in the samples. Hence, this leads to a degradation in
the model’s performance. For instance, authors of [19] noticed a high
redundancy in datasets CIFAR-10, CIFAR-100 and ImageNet. Through experiments,
they proved that the classification accuracy drops between 9% and 14% compared
to the original redundancy-free model. Hence, it is critical to carefully
select FL participants with high-quality and very low-redundant datasets.

Aiming to improve the performance of the UAV-enabled FL model, we propose here
a novel participant selection scheme, a.k.a., data-efficient energy-aware
participant selection strategy (DEEPS), that prioritizes participants with
high data diversity and sufficient battery capacity to handle local training.
To the best of our knowledge, this work is among the firsts to investigate
strategic FL UAV participant selection, with respect to data and UAV
constraints. Our main contributions can be summarized as follows:

  1. 1.

We set up the UAV-enabled edge FL system model, including the FL model, the
UAV communication, mobility, and energy consumption models, and the UAV edge
computing model.

  2. 2.

Following the sub-division of the UAVs’ operating area into sub-regions, we
propose a novel FL UAV participant selection scheme, based on the structural
similarity index measure (SSIM) average score of its local dataset and its
power consumption profile.

  3. 3.

Through experiments on the Fire Luminosity Airborne-based Machine learning
Evaluation (FLAME) dataset [20], we prove the superiority of our selection
scheme, in terms of FL model accuracy, training time, and UAV energy
consumption, compared to the random selection benchmark.

This paper is organized as follows. Section II examines the related work. The
system model is introduced in Section III, while the proposed FL participant
selection scheme is provided in Section IV. Then, performance evaluation is
conducted in Section V. Finally, Section VI concludes the paper.

##  II Related Works

FL is an emerging paradigm that only recently it started to be investigated in
the context of UAV networks. In [21], a serverless architecture for UAV
networks, a.k.a., decentralized FL for UAV networks (DFL-UN) was proposed. It
consists of sharing the UAV local model with the one-hop UAV neighbors.

Decentralized serverless approaches converge relatively fast when the number
of participants is small, i.e., their scalability is very limited, as
discussed in [21]. To tackle this issue and ensure distributed FL scalability,
participant selection protocols are essential. For instance, authors of [16]
proposed a blockchain-based collaborative UAV-based FL architecture to
securely exchange local model updates. Then, UAVs’ privacy is preserved by
applying local differential privacy. Through simulations, the proposed
approach is proven to effectively improve utilities for UAVs, ensure high-
quality model sharing, and guarantee FL privacy protection. However, this
approach did not take into account the UAVs’ power constraints in the
participants’ selection process. In [22], battery-constrained federated edge
learning (FEEL) in UAV-enabled Internet of Things (IoT) is investigated, in
which UAVs can modify their running CPU frequencies to extend battery life and
avoid falling out of FL training prematurely. This system is further optimized
to reduce a linear combination of latency and energy consumption, by jointly
allocating the computational resources and wireless bandwidth based on a deep
deterministic policy gradient (DDPG) strategy. Simulation results show that
the proposed method outperforms the benchmark ones, in terms of FL
convergence, latency, energy consumption, and system cost. Authors in [23]
proposed a semi-supervised FL (SSFL) framework for privacy-preserving UAV
image recognition, where a federated mixing (FedMix) strategy is used to
improve the naive combination of FL and semi-supervised learning under the
scenarios of labels-at-client and labels-at-server. To alleviate the
statistical heterogeneity problem, the federated frequency (FedFreq)
aggregation rule is proposed, which adjusts the weight of the corresponding
local model according to the client’s participation frequency in training. In
[24], a heterogeneous wireless communication architecture is proposed for
collaborative private medical analytics where UAVs utilize the information
gathered by individual end-users (EUs) via wearables and environmental
sensors. In FL, an asynchronous weight update mechanism is proposed to prevent
repeated learning and conserve the UAVs’ and EUs’ limited networking and
computing resources.

##  III System Model

Our system model is depicted in Fig. 1. It is composed of several regions,
where in each region $r$ a BS is paired to an edge computing (EC) server to
serve ground users and also $N$ UAVs deployed in the BS’s geographic area. The
UAVs can also act as edge nodes, but with lower capabilities than the EC
server. For the sake of simplicity, we assume that UAVs are uniformly
distributed on the horizontal plane while hovering at the same altitude.
Finally, EC management is realized by a regional edge controller hosted within
the BS.

Figure 1: System model.

A user in a given region can request a service relative to any region $r$. The
service is characterized by its type that describes the FL task, maximum
number of global FL rounds $n_{r}^{\rm max}$, and required number of UAVs
$N_{r}$ to participate in the FL task. Indeed, we assume that the data
required to execute the user’s request is collected by the UAVs in their
region of interest $r$, thanks to their on-board equipment, including high-
resolution cameras, GPS, LiDAR, etc., and that the latter are participants in
FL training. Then, the EC server associated with the corresponding BS acts as
the FL aggregator.

###  III-A FL Model

FL is a distributed ML technique used to train a shared model while
maintaining the participants’ privacy. Instead of sending raw data to an EC
server, FL participants, in our case UAVs, can train their datasets locally.
The EC server gathers the local models from participating UAVs to generate a
global model. For a learning task, each one of the participating $N_{r}$ UAVs
collects a set of data to create the training dataset $d_{r}^{u}$ with size
$|d_{r}^{u}|$, where $u$ identifies UAV $u$ and $|\cdot|$ is the cardinality
operator. UAV $u$ trains its own neural network (NN) model with a subset of
$d_{r}^{u}$, denoted $d_{r,k}^{u}$, during $T_{e}$ epochs (equivalent to FL
round $k$, with $k=1,\ldots,n_{r}^{\max}$), then sends back its model update
$\omega_{u}$ to its EC server to train the global model $\omega$. The global
model is updated by aggregating the incoming updates. Then, it transmits the
generated model parameters to the participating UAVs for another training
round. These steps are repeated until the model converges or $n^{\max}_{r}$ is
reached. Finally, the result is sent back to the user that triggered the
request. If the latter lies within a different region than the UAVs, the
result is sent between the regions through the wired BSs’ backbone, e.g.,
fiber optic cables.

Typically, the loss function captures the model’s error on training samples.
Let $F_{u}$ be the local loss function for the participating UAV $u$ and
$f_{s}$ is the loss function on a single data sample $s\in d_{r,k}^{u}$.
Consequently, in round $k$

$\small F_{u}(\omega_{u})=\frac{1}{|d_{r,k}^{u}|}\sum_{s\in
d_{r,k}^{u}}f_{s}(\omega_{u}).$ (1)

For the global model, the loss function in round $k$ can be expressed by

$\small
F(\omega)=\sum_{u=1}^{N_{r}}\frac{|d_{r,k}^{u}|}{|d_{r,k}|}F_{u}(\omega_{u}),$
(2)

where $d_{r,k}=\cup_{u=1}^{N_{r}}d_{r,k}^{u}$. The objective of the local and
global models is the minimize the loss function by optimizing and updating the
NN parameters.

###  III-B Communication Model

We assume that the UAVs are hovering in their respective regions in a non-
turbulent environment, e.g., no wind, when a task is requested. With collected
$d_{r}^{u}$ data, training for $T_{e}$ epochs is run by UAVs. Afterward, NN
update parameters are sent to the corresponding BS/EC server through the
wireless channel. For the sake of simplicity, we assume that wireless channels
are reciprocal, and follow the air-to-ground free-space path-loss model, i.e.,
the channel coefficient between UAV $u$ and BS $b$ is written as

$\small
h_{ub}=h_{bu}=\sqrt{\beta_{0}}d_{ub}^{-\frac{\alpha_{ub}}{2}},\;\forall
u,\forall b,$ (3)

where $\beta_{0}$ is the reference channel gain, $d_{ub}$ is the 3D distance
between UAV $u$ and BS $b$, and $\alpha_{ub}$ is the path-loss component
expressed as

$\small\alpha_{ub}=\frac{a_{1}}{1+a_{4}\exp\left({a_{3}\left(\theta_{ub}-a_{4}\right)}\right)}+a_{2},\;\forall
u,\forall b,$ (4)

with $a_{1},\ldots,a_{4}$ constants defined in [25] and $\theta_{ub}$ is the
elevation angle between the BS and UAV. Given that $P_{b,u}$, $P_{u}$, and
$W_{u}$ are the power of BS $b$ to transmit to UAV $u$, UAV $u$’s transmit
power, and reserved bandwidth to communicate to/from UAV $u$, respectively,
then the channels’ capacities for FL data exchange can be given by

$\small
R_{u,b}=W_{u}\log_{2}\left(1+\frac{P_{u}|h_{ub}|^{2}}{W_{u}\sigma^{2}}\right),\;\forall
u,\forall b,$ (5)

and

$\small
R_{b,u}=W_{u}\log_{2}\left(1+\frac{P_{b,u}|h_{bu}|^{2}}{W_{u}\sigma^{2}}\right),\;\forall
u,\forall b,$ (6)

where $\sigma^{2}$ is the unitary power of the additive white Gaussian noise
(AWGN).

###  III-C FL Latency

FL training requires running a number of global training rounds, for which
local training and data transfer between participating UAVs and an associated
EC server occur. Each UAV spends time to train its local model for $T_{e}$
epochs, thus, training time, denoted $t_{r}^{u}$, can be written as

$\small t_{r}^{u}=T_{e}\frac{\kappa|\bar{d}_{r}^{u}|}{\gamma_{u}},\;\forall
u,$ (7)

where $|\bar{d}_{r}^{u}|=\frac{|d_{r}^{u}|}{n_{r}^{\max}}$, assuming that the
sizes of the training subsets are equal for UAV $u$. $\kappa$ is the number of
CPU-cycles needed to process the samples, and $\gamma_{u}$ is the CPU-
frequency of the UAV. Assuming negligible transmit time over the fiber links,
negligible aggregation time at the EC server, and orthogonal access to the
wireless communication channel, then the communication times from/to UAV $u$
and associated BS/EC server are given by

$\small t_{u,b}=\frac{Mz}{R_{u,b}},$ (8)

and

$\small t_{b,u}=\frac{Mz}{R_{b,u}},$ (9)

where the number and size of local NN parameters are denoted by $M$ and $z$,
respectively. Consequently, the duration of one global FL round at the EC
server associated with BS $b$ is

$\small
t_{r}^{b}=\max_{u\in\mathcal{U}_{r}}\left(t_{r}^{u}+t_{u,b}+t_{b,u}\right),$
(10)

where $\mathcal{U}_{r}$ is the set of participating UAVs in FL training.

###  III-D FL Energy Consumption

Typically, a UAV spends most of its energy flying/hovering, while a smaller
amount is dedicated to other services such as communication and computing. For
the sake of simplicity, we ignore the energy consumption related to flying and
focus only on the energy consumed for FL111This assumption is acceptable since
we assumed that UAVs are hovering in a non-turbulent environment, thus the
hovering power is a constant for the same type of UAVs.. The UAV’s consumed
energy for one global FL round is composed of the energy needed to train the
local model, denoted $E_{r}^{u}$, in addition to the energy for transmissions
$E_{u,b}$, where

$\small E_{r}^{u}=\chi_{u}t_{r}^{u}{\gamma_{u}}^{3},$ (11)

and

$\small E_{u,b}=P_{u}t_{u,b},$ (12)

with $\chi_{u}$ is the energy consumption of the UAV’s CPU chips [22]. Since
the BS/EC has an unlimited source power, we ignore its consumption for
communication and computing.

##  IV Problem Formulation

On one hand, correlated data may potentially reduce the accuracy of a model.
In certain instances, the model may overfit the correlated data, which has a
negative impact on the accuracy of each UAV participant $u$. On the other
hand, the energy usage of each UAV is limited by its battery capacity, which
would reduce the number of participations in FL. Our objective is to trade-off
between two conflicting objectives, namely improving the FL accuracy, while
reducing energy consumption. Let $\textbf{x}=[x_{1,k},\ldots,x_{N,k}]$ be the
vector of binary variables that indicate the participation or not of UAVs from
the set of a region $r$ in a training round $k$, where $N$ is the number of
available UAVs. Also, let $B_{u,k}$ be the residual battery capacity of UAV
$u$ at FL round $k$, and $B_{\textrm{max}}$ is the maximal battery capacity of
any UAV. The problem of selecting the best participants while accounting for
energy consumption and local accuracy, denoted $A_{u}$, can be formulated as
follows:

$\displaystyle\max_{\mathbf{x}}$ $\displaystyle\sum_{u=1}^{N}x_{u,k}\left(\xi
A_{u}+(\xi-1)(B_{u,k}/B_{\textrm{max}})\right),$ ($\mathcal{P}1$) s.t.
$\displaystyle x_{u,k}(E_{r}^{u}+E_{u,b})\leq B_{u,k},\;\forall u=1,\ldots,N,$
($\mathcal{P}1$.a) $\displaystyle x_{u,k}\in\\{0,1\\},\forall u-1,\ldots,N,$
($\mathcal{P}1$.b) $\displaystyle\sum_{u=1}^{N}x_{u,k}=N_{r},$
($\mathcal{P}1$.c)

where $\xi$ and $(\xi-1)$ are wighting factors of the sub-objectives, with
$\xi\in[0,1]$. ($\mathcal{P}1$.a) refers to the energy consumption constraint,
while ($\mathcal{P}1$.b) explains the binary nature for participants
selection. Finally, ($\mathcal{P}1$.c) ensures that $N_{r}$ UAVs participate
in each FL round. Problem $\mathcal{P}1$ is complex to solve due to the
intractable expression of the accuracy. Thus, we opt in what follows for a
heuristic approach, where a modification of the objective is needed.

##  V Proposed Participant Selection Strategy: DEEPS

According to [26], UAVs with high-quality datasets, i.e., with less correlated
data and adequate training sample size, can result in efficient local model
updates and faster FL convergence. Based on that, we propose a novel
participant selection strategy, called DEEPS, aiming to optimally select UAVs
for FL training.

To provide high-quality local datasets, we propose to select UAVs in distinct
sub-regions of $r$, while taking into account their battery capacity to
sustain FL training up to convergence or to $n_{r}^{\max}$ FL rounds. For the
sake of simplicity, we assume that the space of region $r$ is partitioned into
3D sub-regions, denoted $\\{sr_{1},\ldots,sr_{M}\\}$ ($M$ is the number of
sub-regions), that host different groups of UAVs. Moreover, we assume an FL
task that relies on datasets built from the UAVs’ onboard camera-captured
images. However, the accuracy $A_{u}$ in ($\mathcal{P}1$) depends on the
quality of used datasets in training. UAVs may collect data frequently, thus
generating large amounts of data with redundant and correlated information.

To attenuate the impact of this phenomenon on the accuracy performance, we
propose to use the structural similarity index measure (SSIM) average score to
measure the similarity or dissimilarity between images in a given dataset.
Conventionally, the Euclidean distance is the basic tool used to evaluate the
correlation of two images $\Gamma_{1}$ and $\Gamma_{2}$. It can also be
expanded to determine the similarity of images with affine pixel intensities
[27]. However, if images $\Gamma_{1}$ and $\Gamma_{2}$ are for instance two
views of the same scene, i.e., obtained from different viewing angles, the
assumption of an affine relationship between image intensity values no longer
holds, thus resulting in a more complex relationship between the pixel
intensities. Moreover, the choice of the right image similarity metric
requires a compromise between image processing speed and efficiency.

In such a context, SSIM is advocated as the most accurate similarity metric
[28]. Indeed, SSIM measures the visual quality and by extension, the
perceptual proximity between images based on subjective quality assessments
through the analysis of vast databases. The SSIM is more accurate than other
metrics, such as the mean squared error (MSE) and the peak signal-to-noise
ratio (PSNR), since it is based on the comparison of structural information
from images rather than pixel-wise error used by MSE and PSNR [28].

SSIM is made up of three components, namely the visual impact of changes in
image brightness, contrast, and any remaining defects, together known as
structural alterations. When combined together with the SSIM function, SSIM
can be calculated as

$\small\text{SSIM}(\Gamma_{1},\Gamma_{2})=\frac{(2\mu_{\Gamma_{1}}\mu_{\Gamma_{2}}+c_{1})(2\sigma_{\Gamma_{1}\Gamma_{2}}+c_{2})}{(\mu_{\Gamma_{1}}^{2}+\mu_{\Gamma_{2}}^{2}+c_{1})(\sigma_{\Gamma_{1}}^{2}+\sigma_{\Gamma_{2}}^{2}+c_{2})},$
(14)

where $\mu_{\Gamma_{1}}$ and $\mu_{\Gamma_{2}}$ represent the mean intensities
of images $\Gamma_{1}$ and $\Gamma_{2}$, respectively. The standard deviations
(square roots of variances) are $\sigma_{\Gamma_{1}}$ and
$\sigma_{\Gamma_{2}}$, and they reflect estimates of the signals’ contrast.
Finally, $\sigma_{\Gamma_{1}\Gamma_{2}}$ is the co-variance of the two images,
whereas $c_{1}$ and $c_{2}$ are two stabilizers acting on a weak denominator
[28]. The values range of SSIM is 0 to 1, where 1 means that images
$\Gamma_{1}$ and $\Gamma_{2}$ are identical, while 0 means that they are
completely different.

Therefore, in order to improve the effectiveness of FL learning, we propose to
consider SSIM of the training subsets as a metric to maximize $A_{u}$. Hence,
we formulate a novel problem $\mathcal{P}2$, where

$\displaystyle\max_{\mathbf{x}}$
$\displaystyle\sum_{u=1}^{N}x_{u,k}\left(\xi\left(1-\text{SSIM}\left(d_{r,k}^{u}\right)\right)+(\xi-1)(B_{u,k}/B_{\textrm{max}})\right),$
($\mathcal{P}2$) s.t. $\displaystyle sr_{i}\cap\\{\text{UAV
}u\;|\;x_{u,k}=1\\}\neq\emptyset,\;\forall i=1,\ldots,M$ ($\mathcal{P}2$.a)
$\displaystyle\text{($\mathcal{P}1$.a)}-\text{($\mathcal{P}1$.c)},$
($\mathcal{P}2$.b) – ($\mathcal{P}2$.d)

where $(1-\text{SSIM}(d_{r,k}^{u}))$ refers to the diversity (or
dissimilarity) of data within subset $d_{r,k}^{u}$, and ($\mathcal{P}2$.a)
guarantees that at least one UAV participant is selected from each sub-region.
Problem $\mathcal{P}2$ is a mixed-integer nonlinear programming (MINLP)
problem, which is NP-hard. To solve it, we propose a heuristic method, as
detailed below.

Specifically, we propose the following UAV participant selection method.
First, the BS/EC server initiates selection by broadcasting a request to UAVs
in all sub-regions to calculate their datasets’ similarity scores with SSIM.

Subsequently, in order to solve Problem $\mathcal{P}2$, the EC server ranks
the UAVs of each sub-region in a descending order based on the utility
function that linearly combines SSIM measure, in order to reflect local
dataset diversity, and the remaining battery level, i.e., UAV $u$ score in FL
round $k$ is

$\small
S_{u,k}=\xi(1-\text{SSIM}(d_{r,k}^{u})+(1-\xi)\frac{B_{u,k}-E_{r}^{u}-E_{u,b}}{B_{\textrm{max}}},\;\forall
u.$ (16)

Next, UAVs with the highest $S_{u,k}$ in each sub-region are selected to
participate in the FL round $k$. Selected UAVs preprocess their subset.
Specifically, through the comparison of the subset samples one by one, a
sample is discarded if it presents high SSIM above a threshold SSIMth, when
compared to another one. By doing so, quasi-redundant data is eliminated and
the storage capacity of the UAV is better managed.

Given the received training parameters and preprocessed data, FL training can
be run until convergence or reaching $n_{r}^{\max}$. It is to be noted that at
the beginning of each FL round, participating UAVs are selected/re-selected in
order to avoid involving UAVs with battery shortages in the FL process.
Furthermore, the proposed solution may be used for FL tasks with different
restrictions. For instance, in applications where energy saving is a priority,
a smaller $\xi$ is preferred, whereas an increased $\xi$ value could
recommended in FL tasks where accuracy is more critical than energy savings.
The proposed DEEPS selection method is summarized in Algorithm 1.

Input: number of UAVs $N$, number of participants $N_{r}$, number of global
rounds $n_{r}^{\rm max}$, $\xi$ utility weight parameter ;

Output : $\omega$ global model after $n_{r}^{\rm max}$ rounds of training ;

Steps

EC server starts the initial global model ;

for _round $k=1,\ldots,n^{r}_{\rm max}$_ do

for _each SubRegion in Region $r$_ do

for _each UAV in SubRegion_ do

Calculate SSIM($d_{r,k}^{u}$) using eq. (14) ;

Update $S_{u,k}$ using eq. (16) ;

Order UAVs in SubRegion from highest to lowest $S_{u,k}$

end Select $N_{r}$ UAVs from the SubRegions with the highest $S_{u,k}$ scores
;

for _each UAV $u\in N_{r}$_ do

if _Subset $d_{u,k}^{r}$ is not preprocessed_ then

Remove redundant data samples from $d_{u,k}^{r}$ using the SSIMth threshold;

end Train local model $\omega_{u}$ ;

Upload $\omega_{u}$ and $B_{u}$ to the EC server ;

end

end Aggregate received local weights and update $\omega$ ;

end

Algorithm 1 DEEPS

##  VI Experiments and Results

###  VI-A Dataset and Preprocessing

The FL experiments conducted here are related to an application that requires
captured images from UAVs, such as in a disaster situation where a scene needs
to be assessed to identify damage, survivors, etc. Specifically, we relied on
the public FLAME dataset [20]. The latter is composed of images taken by UAVs
during a controlled pile burn in Northern Arizona, USA. It contains images
with fire (fire images) and others without any fire (non-fire images). Also,
it includes continuous frames taken from videos captured by the UAVs, where
the images demonstrate significant similarities. An example is illustrated in
Fig. 2 for which SSIM$=0.85$.

To reflect the sub-region partitioning, UAVs that are in the same sub-region
have similar viewpoints from the FLAME dataset and each local dataset is split
into 80% for training and 20% for testing. Moreover, federated learning
performance is influenced by many sorts of data distributions from clients. To
exhibit the non-i.i.d. behavior expected of FL datasets, we vary the size of
local datasets between 500 and 1000 for each UAV with a uniform distribution
of samples in each class.

Figure 2: Comparison of two images from FLAME dataset.

In this experiment, SSIM ensures that contiguous frames in a local dataset
have enough dissimilarities. Hence, we have defined three different methods
for selection, namely random selection (equivalent to randomly picking UAV
participants), DEEPS based on SSIM${}_{th}=0.5$, and DEEPS based on
SSIM${}_{th}=0.1$.

###  VI-B Experimental Setup

We assume a region $r$ consisting of a BS/EC server serving $10$ aerial sub-
regions. For the sake of simplicity, we assume that $N$ UAVs are uniformly
distributed in these sub-regions with batteries $B_{u}\in[10^{3},10^{4}]$
Joules (J), $\forall u=1,\ldots,N$. Similarly to [22], we assume that
$\gamma_{u}=10$ MHz, $\kappa=7\times\;10^{4}$ CPU cycles, $\chi_{u}=10^{-22}$
Watt, $P_{u}=0.28$ Watt, and $P_{b,u}=1$ Watt, $\forall u=1,\ldots,N$. We
consider that diversity in the local datasets of participants is equally
important to the residual energy for the FL task $\tau_{r}$, therefore, we set
$\xi=0.5$. Based on DEEPS, we define a first scenario, called Scenario 1,
where $N_{r}=10$ UAVs will be selected from $N=40$ UAVs in each round to
participate in FL training for at most $n_{r}^{\max}=200$ rounds. We assume
here that a single UAV is selected from each sub-region. A second scenario
(Scenario 2) is designed where $N=100$ and $N_{r}=20$ UAVs are selected among
them, where 2 UAVs are selected from each sub-region.

The aggregator and UAV FL participants use the same convolutional neural
network (CNN) model. Each participant trains its model to perform an FL
classification task (presence or absence of fire) in the images. Specifically,
the binary classification model used here is a small version of Xception
network [29] [30], i.e., a deep CNN architecture that involves depthwise
separable convolutions. The CNN model is composed of an input layer, two
hidden layers, and an output layer. The input layer is of size $250\times
250\times 3$, which reflects the size of input images in pixels and the three
red-green-blue (RGB) color channels. The RGB values are scaled to float values
between 0 and 1. Hidden layers are composed of 2-dimensional (2D)
convolutional blocks with a size of 8 and a stride of $2\times 2$, where each
block is followed by a Rectified Linear Unit (ReLU) and batch normalization
[31]. ReLU is utilized as the activation function to increase the network’s
fitting ability, while batch normalization equalizes the inputs to each layer
for each mini-batch to accelerate the learning process. The output layer is a
fully-connected one with the Sigmoid function to normalize the output to a
probability distribution. The learning rate is set to $10^{-2}$. Finally, the
neural network is trained by the Adam optimizer, and the cross-entropy loss
function is adopted to measure the classification performance. Finally, the
batch size is set to 32.

Figure 3: Global accuracy vs. number of FL rounds.

Fig. 3 compares the performance of the proposed selection strategy DEEPS to
those of the random selection benchmark, in terms of global accuracy, for both
scenarios 1 and 2. Random selection exhibits the worst accuracy performance,
mainly due to the high risk of training with correlated samples in both
scenarios, especially when participants are selected from the same sub-region
or from UAVs with highly redundant data. In contrast, DEEPS is superior due to
its efficient preprocessing based on SSIM and sub-region-based UAV participant
selection. Also, a lower SSIMth improves further the training accuracy that
reaches 90% for SSIM${}_{th}=0.1$, against 85% for SSIM${}_{th}=0.5$ and 77%
for random selection in Scenario 1.

In Scenario 2, higher performances are achieved with the preference for DEEPS
with SSIM${}_{th}=0.1$. Indeed, the availability of a higher number of UAVs
and selecting two UAVs per sub-region favors a more accurate FL training
without delaying convergence. Finally, we notice that generally, DEEPS
achieves convergence faster than random selection in both scenarios.

TABLE I: Performance comparison for different participant selection methods Scenario | Selection Criteria |  $\lambda_{t}$ (s) |  $\chi_{r}$ |  $\rho_{t}$ (min)
---|---|---|---|---
Scenario 1 | Random Selection | 120.33 | 123 | 245.9
| DEEPS (SSIM=0.5) | 58.21 | 97 | 93.71
| DEEPS (SSIM=0.1) | 51.02 | 88 | 77.46
Scenario 2 | Random Selection | 137.89 | 153 | 343.4
| DEEPS (SSIM=0.5) | 70.55 | 115 | 131.2
| DEEPS (SSIM=0.1) | 63.85 | 96 | 78.61

Table I illustrates the performance of DEEPS against the benchmark selection
method in terms of average round time $\lambda_{t}$, number of rounds until
convergence $\chi_{r}$, and elapsed time until convergence $\rho_{t}$. In the
first scenario, DEEPS executes each FL round in less than 60 seconds, for any
SSIMth value, which is half of the required time by random selection to
complete a single FL round. Indeed, this is due to the preprocessing step in
DEEPS that reduces the dataset size and guarantees the use of only informative
samples. In contrast, random selection chooses UAVs with unprocessed datasets
and potentially with redundant information. A similar conclusion can be drawn
for Scenario 2, where the convergence time is slightly higher than in Scenario
1. This is explained by the increased number of UAV participants in the FL
system.

Figure 4: Energy consumption vs. number of FL rounds.

In Fig. 4, we present the energy consumed by participating UAVs of DEEPS and
random selection benchmark, in joules (J). For Scenario 1, the energy
consumption is high at first, then it drops after a number of FL rounds for
any participant selection method, e.g., for random selection, energy
consumption drops by 900 J after 100 rounds. This is caused by the high
battery drainage that forces a number of UAVs to drop from FL training.
Nevertheless, UAVs using DEEPS consume the smallest amount of energy, e.g.,
about 400 J (resp. 300 J) for SSIM${}_{th}=0.5$ (resp. SSIM${}_{th}=0.1$),
with a slight drop after the 105th (resp. 132th) round by 76 J (resp. 30 J)
for SSIM${}_{th}=0.5$ (resp. SSIM${}_{th}=0.1$). Indeed, data preprocessing
and selection of UAVs with higher $B_{u,k}$ levels in DEEPS ensure that FL
training consumes less energy and UAVs last longer in FL than random
selection. In contrast, random selection has to execute training with more
data, in addition to using UAVs that might have limited battery capacities.
Similar conclusions can be drawn for Scenario 2 where we notice a stable
energy consumption for DEEPS compared to the benchmark. However, energy
consumption is higher than for Scenario 1 due to the involvement of a higher
number of UAVs in FL training.

##  VII Conclusion

In this paper, we investigated the problem of UAV participant selection for
FL, while taking into account the datasets’ similarity/dissimilarity and
energy consumption. By adopting SSIM similarity score, applying dataset
preprocessing, sub-dividing the UAVs’ 3D region, and defining a UAV score
function, we designed a novel participant selection method, called DEEPS.
Through experiments, we demonstrated the superior performances of DEEPS
compared to the random selection benchmark, in terms of FL accuracy, training
time, and energy consumption. In future work, we will focus on the optimal
partitioning of the UAVs’ 3D region and on FL operation with UAV mobility and
heterogeneous equipment.

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