# SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable
Smart Farms

Dian Chen<EMAIL_ADDRESS>0009-0000-7641-454X Virginia Tech7054 Haycock RdFalls
ChurchVirginiaUSA22043 ,  Paul Yang  Virginia TechBlacksburgVirginiaUSA
<EMAIL_ADDRESS>,  Ing-Ray Chen  Virginia Tech7054 Haycock RdFalls
ChurchVirginiaUSA<EMAIL_ADDRESS>,  Dong Sam Ha  Virginia
TechBlacksburgVirginiaUSA<EMAIL_ADDRESS>and  Jin-Hee Cho  Virginia Tech7054
Haycock RdFalls ChurchVirginiaUSA<EMAIL_ADDRESS>
(2024)

###### Abstract.

We propose a novel energy-aware federated learning (FL)-based system, namely
SusFL, for sustainable smart farming to address the challenge of inconsistent
health monitoring due to fluctuating energy levels of solar sensors. This
system equips animals, such as cattle, with solar sensors with computational
capabilities, including Raspberry Pis, to train a local deep-learning model on
health data. These sensors periodically update Long Range (LoRa) gateways,
forming a wireless sensor network (WSN) to detect diseases like mastitis. Our
proposed SusFL system incorporates mechanism design, a game theory concept,
for intelligent client selection to optimize monitoring quality while
minimizing energy use. This strategy ensures the system’s sustainability and
resilience against adversarial attacks, including data poisoning and privacy
threats, that could disrupt FL operations. Through extensive comparative
analysis using real-time datasets, we demonstrate that our FL-based monitoring
system significantly outperforms existing methods in prediction accuracy,
operational efficiency, system reliability (i.e., mean time between failures
or MTBF), and social welfare maximization by the mechanism designer. Our
findings validate the superiority of our system for effective and sustainable
animal health monitoring in smart farms. The experimental results show that
SusFL significantly improves system performance, including a $10\%$ reduction
in energy consumption, a $15\%$ increase in social welfare, and a $34\%$ rise
in Mean Time Between Failures (MTBF), alongside a marginal increase in the
global model’s prediction accuracy.

Smart farm, energy-aware, federated learning, deep learning, solar sensors,
sustainability.

††copyright: acmcopyright††journalyear: 2024††doi:
10.1145/xxxxxxx.xxxxxxx††conference: WiSec ’24: 17th ACM Conference on
Security and Privacy in Wireless and Mobile Networks; May 27–May 30, 2024;
Seoul, Korea††booktitle: WiSec ’24: 17th ACM Conference on Security and
Privacy in Wireless and Mobile Networks, May 27–May 30, 2024, Seoul,
Korea††price: 15.00††isbn: 978-1-4503-XXXX-X/21/06

##  1\. Introduction

###  1.1. Motivation & Research Goal

Smart farm technologies have shown a revolutionary shift in agricultural
practices to meet the increasing global food demand, enhancing resource
efficiency and promoting sustainability. This shift is propelled by
integrating cutting-edge technologies, such as the Internet-of-Things (IoT),
wireless sensors, artificial intelligence (AI), and data analytics. These
technologies facilitate real-time monitoring, enable data-driven decision-
making, and automate processes to boost productivity, conserve resources, and
improve operational efficiency. In the realm of smart farming, each animal is
equipped with a solar-powered sensor that gathers data on the animal’s
condition. This data is periodically sent to edge devices, such as gateways,
via Long-Range (LoRa) transmission and subsequently to a cloud server. This
infrastructure allows farmers to efficiently monitor their livestock and
manage farm operations. Although the adoption of solar-powered sensors
presents labor-saving and environmental benefits, it also introduces
significant challenges. Chief among these is the need to maintain a delicate
balance between system performance and the sustainability of the wireless
sensors’ energy levels. Addressing this balance is crucial for the successful
implementation and long-term viability of smart farming technologies.

To advance the automation capabilities of smart farming systems, a cloud
server can apply a deep learning (DL) model to analyze data transmitted from
gateways to detect diseases like mastitis in cattle. This approach
acknowledges the potential of federated learning (FL) to enhance disease
prediction in smart farms while preserving data privacy and supporting
sustainable agricultural practices. FL’s efficacy, particularly in terms of
prediction accuracy, is largely influenced by the active participation of
local devices, such as sensors. In smart farming environments, the limited
energy supply of sensors – restricted by the size of their solar panels –
poses a significant challenge. This limitation affects not only the local
training capabilities of these devices but also the overall performance of the
FL system. In addition, the integration of sophisticated technologies
increases the susceptibility of smart farms to cyber threats, including
attacks that aim at compromising data integrity and disrupting the learning
process in AI models. To counter these issues, we introduce a robust federated
learning algorithm, SusFL, specifically designed for smart farms to optimize
the prediction accuracy of animal diseases and prolong the lifespan of the
sensor network, while providing resilience against cyber threats. This
innovative approach represents a strategic advancement in maintaining the
balance between operational efficiency, system sustainability, and security in
smart farming systems.

###  1.2. Key Contributions

Our work made the following key contributions:

  1. (1)

Sustainable FL with energy-efficient client selection via mechanism design: We
adopt a strategy rooted in game theory, specifically mechanism design, to
enhance the sustainability of smart farming systems. This approach focuses on
selecting clients (i.e., sensor-equipped animals) in an energy-efficient
manner to optimize the system’s overall sustainability. The effectiveness of
this method is quantitatively assessed using a reliability metric known as
Mean-Time-Between-Failures (MTBF) to build a smart farm that not only
conserves energy but also maintains high operational reliability.

  2. (2)

Pioneering FL for disease detection in smart farm animals: Our work represents
the inaugural exploration into FL-based monitoring systems designed
specifically for smart farms by identifying illnesses in livestock. Unlike
previous studies, which did not apply FL for animal disease detection, our
approach leverages comprehensive experiments utilizing data from the Internet
of Animal Health Things (IoAHT). These experiments are focused on clinical
mastitis in cows, thereby providing a solid foundation for validating the
effectiveness of FL in real-world agricultural settings.

  3. (3)

Robust hierarchical FL under adversarial attacks: Our research addresses
adversarial attacks in smart farm systems using a hierarchical FL framework to
maintain high global prediction accuracy for animal health through data
quality-aware aggregation. Unlike previous studies, we evaluate the effect of
attacks on both prediction accuracy and sensor node energy efficiency in a
resource-constrained environment. Utilizing real-world data from Virginia
Tech’s smart farm, we simulate authentic conditions to validate our approach.

  4. (4)

Experimental Validation of SusFL: Our results demonstrate SusFL’s enhanced
performance, achieving a 10% decrease in energy use, 15% boost in social
welfare, 34% higher MTBF, and slightly improved prediction accuracy in the
global model.

##  2\. Background & Related Work

In this section, we introduce the fundamentals of federated learning (FL) and
discuss existing approaches of FL-based energy-efficient systems and
monitoring solutions in IoT-based smart environments such as smart farm
systems.

###  2.1. Federated Learning

Figure 1. Key steps in FL training process

FL emerges as a cutting-edge machine learning (ML) paradigm that facilitates
collaborative model training across multiple data providers, aiming to
construct a high-quality, centralized model without compromising data privacy
(Konečný et al., 2016). As illustrated in Figure 1, the FL framework
encompasses a central server hosting the global model and numerous client
devices, each maintaining a local model. Within this ecosystem, we consider
$N$ distinct data providers, denoted as $\\{C_{1},\ldots,C_{N}\\}$, each
possessing a unique dataset $\\{D_{1},\ldots,D_{N}\\}$. The training of an ML
model $M_{FED}$ under the FL protocol involves the collective effort of all
participating data providers. Here, each provider $C_{i}$ exclusively accesses
its dataset $D_{i}$ to contribute to the global learning process (Yang et al.,
2019).

The performance of an FL algorithm is quantified in terms of $\delta$-accuracy
loss, formally expressed as:

(1) $|P_{FED}-P_{SUM}|<\delta,\quad\delta\in\mathbb{R},$

where $P_{FED}$ represents the performance of the model trained via the FL
algorithm, and $P_{SUM}$ signifies the performance achievable if the model
were trained on the aggregated dataset, $D=D_{1}\cup\ldots\cup D_{N}$. This
measure of accuracy loss $\delta$, a real number, serves as a critical
benchmark to assess the efficiency and effectiveness of the FL approach in
approximating the ideal outcome achieved through conventional centralized
training methods.

FL systems are categorized based on the distribution characteristics of the
data, primarily into two types: horizontal FL (HFL) and vertical (VFL) (Nguyen
et al., 2021). HFL applies to scenarios where data providers possess datasets
that overlap in feature space but are distinct in sample space. Conversely,
VFL is appropriate for situations where the datasets share a common sample
space but diverge in feature space. Additionally, federated transfer learning
(FTL) addresses the more complex scenarios where data holders’ datasets vary
both in feature and sample spaces.

To advance data privacy and mitigate communication latency within FL
methodologies, the concept of hierarchical FL (HierFL) has emerged as a
significant innovation (Liu et al., 2020). Traditional FL architectures are
characterized by a two-layer structure, often facing scalability challenges.
In response, Liu et al. (2020) proposed augmenting the FL framework with an
additional layer, thereby introducing an intermediary phase of edge server
aggregation between clients and cloud aggregation. This phase acts as a
conduit for data processing between the initial client contributions and the
final cloud aggregation. By integrating hierarchical FL, we aim to construct a
monitoring system for sustainable smart farms that leverages this three-tiered
architecture for improved efficiency, enhanced data privacy, and reduced
communication delays.

###  2.2. Energy-Efficient FL for Smart IoT Systems

Yang et al. (2020) developed an energy-efficient Federated Learning (FL)
algorithm for latency-sensitive environments like WSNs, optimizing factors
like time allocation and computation frequency to minimize energy consumption
and FL completion time. Similarly, Pham et al. (2022) proposed an Energy-
efficient FL (E2FL) algorithm tailored for UAVs, addressing nonconvex
optimization challenges by breaking them into manageable subproblems.
Additionally, to address the challenge of non-identical independent
distribution in efficient FL, Kim and Wu (2021) introduced AutoFL, utilizing
reinforcement learning (RL) to select optimal devices for FL participation,
thus enhancing the training process efficiency. Do et al. (2021) utilized a
deep RL (DRL)-based framework to optimize long-term FL performance in
environments constrained by resources. They deployed DRL agents on each UAV to
dynamically select the best combinations of location, computing speed,
transmit power, and bandwidth to maintain energy usage within set budgets for
each user. Moreover, Wang et al. (2020) innovated an FL scheme using a support
vector machine (SVM) to determine the best user association, power allocation,
and task allocation strategies to minimize energy usage. Li et al. (2021)
focused on optimizing energy consumption in mobile devices constrained by
battery life. This approach balanced local training and data transmission
effectively, ensuring the FL model’s convergence while preserving the system’s
overall energy integrity.

Despite the existing solutions for energy-efficient FL in resource-constrained
environments above, current methodologies exhibit a significant gap as
follows. First of all, they do not account for dynamic and fluctuating energy
conditions, such as those encountered with solar-powered sensors. Further,
there is a lack of strategies that optimize the FL process through client
selection based on a valuation framework that includes criteria like energy
level and data quality. This oversight presents a critical opportunity for
innovation, suggesting the need for a more adaptive and nuanced approach to FL
in environments where energy availability varies significantly over time and
across devices. By integrating client selection mechanisms that consider the
dynamic energy states and the inherent data quality of participating devices,
our work aims to significantly enhance the efficiency and effectiveness of FL
systems, especially in scenarios reliant on renewable energy sources.

###  2.3. FL-based Monitoring Systems

Nguyen et al. (2019) introduced a security monitoring system tailored for IoT
devices, utilizing an FL framework to identify anomalies within IoT networks
based on device-specific communication patterns. Similarly, Sun et al. (2020)
implemented segmented FL within a Local Area Network (LAN) Security monitoring
system, enabling local models to adapt to network dynamics. While both studies
demonstrated their approaches through extensive experimentation, they notably
lacked a comprehensive set of metrics to adequately assess the performance of
their monitoring systems.

Wu et al. (2022) developed FedHome, an FL-based health monitoring system
designed to predict human conditions accurately while ensuring data privacy.
They used a generative convolutional autoencoder (GCAE) to minimize the
computational and communication overhead of parameter exchange between cloud
servers and edge devices. In a similar vein, Khoa et al. (2021) employed an
autoencoder (AE) that integrates an Encode Depthwise Convolutional Network
(EDCN) with a Decoder to reduce feature dimensions and personalize models,
aligning with the objectives of efficient and tailored FL applications. Elayan
et al. (2022) introduced a deep FL (DFL) framework aimed at IoT-based
healthcare applications, achieving notable success in monitoring accuracy and
maintaining data privacy. Their framework is distinctive for splitting the FL
process into three phases: initialization, data collection, and model update,
streamlining the learning process.

Anand et al. (2023) explored the effectiveness of FL in a Smart Cities Street
Light Monitoring application. While the FL-based approach did not outperform
other machine learning models in terms of accuracy, it significantly enhanced
communication efficiency and data privacy in real-world deployments.
Furthermore, Fan et al. (2022) proposed an innovative FL architecture for the
Internet of Medical Things (IoMT), named FLDIoMT, broadening the scope and
scalability of IoMT frameworks as well as addressing critical data security
concerns. They incorporated data reputation in the global model update
process, ensuring that only contributions from local models with significant
value are considered.

While various strategies have been proposed to create secure and efficient
monitoring systems via FL, it is evident that addressing system
vulnerabilities extends beyond ensuring data privacy. Comprehensive security
measures are essential to safeguard the normal operation of such systems,
underscoring the complexity and multifaceted nature of challenges in FL
deployment.

###  2.4. FL-based Smart Farms

Idoje et al. (2023) utilized FL for crop classification in smart farms, using
climatic features and crop types as labels, employing a hyper-tuned Federated
Averaging algorithm for precise predictions within a decentralized network,
validated through a PySyft, Pytorch, and Syft Testbed. Siniosoglou et al.
(2023) implemented a Centralized FL System (CFLS) for time series forecasting
in smart agriculture, using Long Short-Term Memory (LSTM) models and FL for
data privacy and efficient learning with extensive data. Manoj et al. (2022)
explored FL in agricultural risk management for crop yield predictions,
showing its superiority in accuracy and privacy over centralized methods.
Vimalajeewa et al. (2021) introduced a service-based FL model to predict milk
quality, proposing sequential and parallel aggregation methods for enhanced
efficiency. While these studies highlight FL’s potential in smart agriculture,
they focus on non-living targets and lack real-world dataset evaluation.
Additionally, they primarily rely on federated averaging, which may be
insufficient for complex scenarios – a gap our work aims to address.

Friha et al. (2022) introduced FELIDS, a FL-based intrusion detection system
(IDS) for securing agricultural IoT systems. FELIDS ensures data privacy
through local model updates without sharing raw data, achieving superior
detection capabilities as evidenced by datasets like CSE-CIC-IDS2018, MQTTset,
and InSDN. On the security enhancement front, Praharaj et al. (2023) proposed
a hierarchical federated transfer learning framework to safeguard cooperative,
smart farming against cyber threats by detecting anomalies and malicious
activities. However, the absence of empirical evidence in their study leaves
the effectiveness and comparative advantage of their framework unverified.

To our knowledge, research on addressing comprehensive security issues in FL-
based monitoring systems for smart environments remains scant. Further
exploration is required in the FL-based smart farming sector. Our pioneering
work introduces an FL-based system for predicting animal diseases, marking the
first instance of utilizing FL to oversee animal health conditions in smart
farming environments. Moreover, our work diverges from existing energy-
efficient FL methodologies by focusing on delivering secure and robust
services. Specifically, we concentrate on energy-aware FL strategies tailored
for resource-limited, solar sensor-equipped smart farm systems, bridging a
critical gap in the literature.

##  3\. Problem Statement

Our smart farm system employs FL to accurately predict animal disease risks
while prolonging system longevity. We conceptualize this system as a
hierarchical FL structure, as depicted in Figure 2. This structure features a
global model hosted on a cloud server, with local models operating on LoRa
gateways (termed as edge devices) and Raspberry Pis mounted on animals
(referred to as clients). For simplicity, models on gateways are designated as
edge models, and those on clients as local models, with the central server
running the global model.

Our primary focus lies on the edge level, where gateways execute global models
and clients manage local models. Each gateway communicates with a specific set
of sensor clients within its communication range. Clients update their models
by transmitting them to the gateway offering the highest utility. Detailed
explanations on client utility estimation are provided in Section 5.1.

Each gateway in our system seeks to enhance the performance of its edge model
by aggregating learning parameters from local models of a carefully chosen set
of clients. This selection process prioritizes security, energy efficiency,
and fairness. Conversely, clients strive to conserve energy to prolong their
operational lifespan while supplying essential updates to improve the edge
model’s effectiveness. Collectively, our system tackles multi-objective
optimization (MOO) challenges through a $\varepsilon$-constraint method,
embodied in a scalarization-based MOO function (Cho et al., 2017), aiming to:

(2) $\begin{gathered}\text{maximize}\quad\mathcal{ACC}(M(s^{*}))\\\
\text{subject to $\mathcal{EC}(s^{*})\geq\varepsilon$}.\end{gathered}$

In our system, $M(s^{*})$ denotes the edge model trained using a selected set
of sensor nodes $s^{*}$ for the FL aggregation process. The
$\mathcal{ACC}(M(s^{*}))$ measures the prediction accuracy of $M(s^{*})$, and
$\mathcal{EC}(s^{*})$ quantifies the energy consumption of these selected
sensor nodes. Both accuracy $\mathcal{ACC}(M)$ and the target accuracy
threshold $\varepsilon$ are normalized within the range $[0,1]$. Our objective
is to minimize $\mathcal{EC}(s^{*})$ while ensuring that
$\mathcal{ACC}(M(s^{*}))$ meets or exceeds $\varepsilon$. This approach
underlines our commitment to developing a sustainable smart farm through
hierarchical FL, namely SusFL, representing sustainable FL.

SusFL incorporates a mechanism design-based client selection for FL
aggregation, designed to withstand adversarial attacks (detailed in Section
4.3). We will elaborate on the system’s design and its components aimed at
achieving sustainability in Section 5.

Figure 2. Hierarchical FL-based network architecture designed for a wireless
solar sensor-based smart farm

##  4\. System model

In this section, we describe the considered network, node, and threat models.

###  4.1. Network Model

Our smart farm system employs a network model that integrates solar-powered
sensors, wearable Raspberry Pis (R-Pis), Long-Range (LoRa) gateways, and a
cloud server, as illustrated in Figure 2. Each animal, such as a cow, is
fitted with solar sensors on their ears to monitor body conditions, with the
data transmitted to nearby R-Pis. A selected group of animals equipped with
R-Pis act as computational clients, processing their data and that from others
to train local models—constituting step 1. LoRa gateways, equipped with edge
servers, receive these local updates, refine the edge model, and forward the
refined model parameters to the cloud server, which then updates the global
model in steps 2 and 3. Our approach, susFL, focuses on optimizing these
processes, particularly for energy-limited devices (i.e., clients). In step 4,
the cloud server dispatches the latest model parameters back to the gateways,
distributing them to the clients within range in step 5. This configuration
capitalizes on LoRa technology to boost IoT connectivity, minimizing costs
while extending the range.

A deep learning (DL) model, deployed across local servers on client devices,
edge servers, and the central server, is designed to assess the risk of
mastitis in animals. It outputs a binary classification: 0 indicates a healthy
cow, while 1 signifies a cow diagnosed with mastitis.

###  4.2. Node Model

In the smart farm network, sensors periodically transmit data to nearby
clients, enabling the training of local models with freshly sensed data. Given
these sensors are solar-powered, their energy levels naturally fluctuate due
to various environmental influences, including the animals’ locations, weather
conditions, and seasonal variations in sunlight exposure. Additionally,
designing FL systems faces significant challenges, such as high communication
and computational costs, along with the need to ensure data privacy.
Consequently, the system needs to be resilient, capable of adapting to the
dynamic and energy-variable environment of the smart farm, and fortified
against potential adversarial attacks.

In our smart farm network, sensors use Bluetooth Low Energy (BLE) to send data
to proximal clients, and these clients, in turn, forward their local updates
to LoRa gateways. This setup employs the LoRa protocol for long-range
communications, effectively covering distances between 5 to 15 $km$ with a
data transfer speed of 27 $kbps$. For shorter distances, up to 100 meters, the
BLE protocol is used, boasting a faster transfer speed of 2 $Mbps$.

For energy consumption, the LoRa radio of SAM R34/35 expends about 170 $mW$
during data transmission, while the BLE radio has a lower consumption rate of
approximately 11 $mW$. A Raspberry Pi’s power usage is 0.117 $W$ per second
when idle, increasing to 0.172 $W$ per second under load. Sensor nodes, once
fully charged, hold an initial energy reserve of 5 $kW$. The charging
efficiency for solar-powered sensors varies with light exposure—about 10
$mW/cm^{2}$ in outdoor settings and 0.1 $mW/cm^{2}$ indoors.

We distinguish between two sensor node types in our analysis:

  * •

Normal sensor node (Texas Instruments, 2016): This node lacks the
computational resources for local model training, instead periodically sending
its data to a Raspberry Pi (R-pi)-based node via BLE.

  * •

R-pi-based sensor node (Caria et al., 2017; Microchip, 2018; PiCockpit, 2023):
This node is capable of gathering data from normal nodes within its range and
training local models. This node decides on its participation in the FL
aggregation by sending its local model parameters to the edge model, thereby
acting as a client within the FL framework.

###  4.3. Threat Model

To understand the vulnerabilities within FL systems, we examine the following
types of adversarial attacks:

  * •

Byzantine attacks disrupt the FL training process by injecting arbitrary
metrics via Stochastic Gradient Descent (SGD) updates (Blanchard et al.,
2017). These attacks primarily target local devices or clients, prolonging
their learning duration or leading to model divergence.

  * •

Backdoor attacks compromise the integrity of edge and global models through
malicious clients that submit altered local model updates (Bagdasaryan and
Shmatikov, 2021). The objective of backdoor attackers is to preserve high
prediction accuracy during testing to evade detection while causing the model
to incorrectly classify a specific target class.

  * •

Collaborative attacks involve multiple compromised clients working together to
degrade the global model’s accuracy (Bagdasaryan et al., 2020). This type of
attack affects both the global model on the central server and the edge models
on gateways. Attackers may adjust training hyperparameters or alter model
weights before these are sent to the edge model. The success rate of backdoor
attacks increases with the proportion of attacker-controlled clients,
surpassing the effectiveness of conventional data poisoning strategies.

To assess the impact of these attacks, we analyze the attack success
probability, denoted as $P_{A}$, which represents the likelihood of an
attacker successfully executing an attack at any given time $t$. Our primary
goal is to create a sustainable FL-based monitoring system characterized by
its resilience and capability of functioning effectively under adversarial
attacks, including Byzantine, backdoor, and collaborative attacks.
Consequently, our work does not concentrate on developing specific defenses
against these attacks. Instead, we emphasize the selection of trustworthy
clients and the secure aggregation of local model parameters to ensure the
system’s tolerance and robustness against such cybersecurity threats.

##  5\. Proposed Approach: SusFL

In the given network, we consider a group of $n$ clients, denoted by
$N=\\{1,\ldots,n\\}$, each possessing a local model eligible for selection.
The cost of including client $i$’s local model in the aggregation process is
represented by $c_{i}$, a known value publicly. With a total budget constraint
of $B$, we aim to ensure that the selected clients maintain adequate energy
reserves after completing a given FL task. To achieve this, we propose to
develop a client selection mechanism, $\mathcal{M}$, aimed at identifying an
optimal subset of clients. This subset will contribute to the FL model
aggregation, optimizing the balance between achieving the desired accuracy of
the edge model and minimizing energy consumption.

Figure 3. Key steps for an edge server (i.e., mechanism designer) to aggregate
local model updates from selected clients

Figure 3 describes the FL model aggregation process in four key steps in our
proposed SusFL system:

  1. (1)

Request for local model updates: Each gateway’s server requests local updates
from clients within its operational area.

  2. (2)

Utility estimation and response: Clients calculate their utility based on the
anticipated energy expenditure for participating in the FL process, as defined
in Eq. (3). Clients with utilities that are equal to or larger than a
predefined threshold $\theta$ communicate their values, as formulated by Eq.
(5), back to the gateway.

  3. (3)

Client selection and notification: The mechanism designer, operating at the
gateway, selects clients for the FL process based on their values and notifies
the chosen clients.

  4. (4)

Local update transmission and model aggregation: Selected clients transmit
their local updates to the gateway’s edge server, aggregating these updates
into an enhanced edge model, focusing on high-quality data as described in
Section 5.3. This model is used to assess the health conditions of cows on the
considered SusFL-based smart farm.

After completing each FL cycle, gateways upload their updated models to the
central server, consolidating them into a new global model. This updated
global model is then disseminated back to the gateways, ensuring continuous
improvement and accuracy of the system’s predictive capabilities. We describe
the key design components of the proposed SusFL in the following sections.

###  5.1. Clients’ Utility Estimation

Upon receiving a local model update request from its gateway, a client
assesses the task’s utility to determine its participation in the aggregation
process. The utility of client $i$ upon receiving this request from gateway
$j$ at time step $t$, denoted as $u_{t}(i,j)$, is calculated by:

(3) $u_{t}(i,j)=e_{t}(i)-ec_{t}(i,j).$

The utility calculation incorporates $e_{t}(i)$, the current energy level of
client $i$, and $ec_{t}(i,j)$, the expected energy consumption of client $i$
when participating in the FL aggregation process with gateway $j$. Both
variables, $e_{t}(i)$ and $ec_{t}(i,j)$, are normalized to the range $[0,1]$
as real numbers. The expected energy consumption, $ec_{t}(i,j)$, is determined
by:

(4) $\displaystyle ec_{t}(i,j)=\underbrace{|D_{i}^{t}|\times r\times
E_{\mathrm{R-pis}}}_{\text{for
training}}+\underbrace{\mathrm{Dis}(p_{i}^{t},p_{j}^{t})\times
E_{T}}_{\text{for transmission}}.,$

The expected energy consumption, $ec_{t}(i,j)$, is calculated based on
$|D_{i}^{t}|$, the volume of data client $i$ uses for local training at time
step $t$, and $r$, the rate at which training time extends per additional data
sample. It also considers $E_{\mathrm{R-pis}}$, the energy required to process
the specified workload per second. The distance between client $i$ and gateway
$j$ during the FL process at time step $t$, denoted as
$\mathrm{Dis}(p_{i}^{t},p_{j}^{t})$, and $E_{T}$, the energy for transmitting
local updates via LoRa, are also factored into the calculation.

The utility function enables client $i$ to evaluate the benefit of
participating in the aggregation process at time step $t$. If participating
would result in the client’s energy being entirely depleted from transmitting
data to the edge model, leading to a utility of $u_{t}(i,j)\leq 0$, the client
will opt out of the aggregation. This decision adheres to the individual
rationality property within mechanism $\mathcal{M}$, ensuring clients
participate only when the utility is positive, thus preserving their normal
operation until a more opportune moment arises. Upon opting to contribute,
client $i$ communicates its value $v_{i}$ to the edge server, which then
assesses client selection. The value $v_{i}$ reflects the data quality of
client $i$’s model update, as determined by (Kang et al., 2019), and
formulated by:

(5) $v_{i}=\frac{\Psi}{\log(\frac{1}{\varepsilon_{i}})},$

The value $v_{i}$ is influenced by $\Psi$, a coefficient related to the number
of local model iterations impacted by local data accuracy, and $\varepsilon$,
which denotes the iteration count for a local model update to maintain a
constant global accuracy. This assessment is supported by theoretical
guarantees based on empirical evaluations rather than hypothetical
expectations. Importantly, $v_{i}$ assumes a negative value if $\Psi>0$ and
$\varepsilon_{i}>1$, indicating that the closer $v_{i}$ is to zero, the higher
its perceived value.

To address the Multi-Objective Optimization (MOO) challenge discussed in
Section 3 and streamline the search space, gateways initially identify clients
whose participation in the selection process could ensure a minimum accuracy
of $\varepsilon$. Consequently, only clients with a value of $v_{i}\geq\theta$
qualify as candidates for aggregation. Here, $\theta$ represents the threshold
that defines the subset of clients eligible for further consideration in the
client selection phase.

###  5.2. Client Selection Mechanism

Mechanism $\mathcal{M}$ is designed to fulfill specific properties,
simplifying our discussion by excluding the notation $j$. This approach
generalizes the utility of client $i$ across any scenario where it receives a
request for a local model update to participate in the FL aggregation of
gateway $j$, as depicted in Figure 3.

  * •

Truthfulness (Bei et al., 2012): $\mathcal{M}$ is truthful if each client
$i$’s dominant strategy is to report true information when

(6) $u(i)\geq u(i^{{}^{\prime}}),$

where $i^{{}^{\prime}}$ is any client’s information that $i^{{}^{\prime}}\neq
i$.

  * •

(Weak) Budget balance (Chawla et al., 2011): $\mathcal{M}$ is budget-balanced
when

(7) $\displaystyle\sum_{i}^{N}\mathcal{COM_{E}}(i)\leq B,$

where $\mathcal{COM_{E}}(i)$ represents the communication cost incurred when
integrating client $i$ into the aggregation. LoRa gateways, not being limited
by energy constraints for model training, could theoretically accept clients’
local updates immediately. However, to minimize energy consumption on the
client side and preserve the operational longevity of sensor nodes, it is
imperative from a global standpoint to restrict the number of communication
rounds. This strategy ensures that clients maintain sufficient energy levels
for regular operations.

  * •

Individual rationality (Gresik, 1991): $\mathcal{M}$ is individual rational if

(8) $\forall i\quad u(i)\geq 0.$

Our utility function is designed to assess the variance between a client’s
present energy status and the energy expenditure associated with contributing
to aggregation $j$. Thus, clients will partake in aggregation $j$ solely if
they anticipate sustaining their energy levels post-participation, ensuring no
energy depletion occurs from engaging in the task for gateway $j$.

The mechanism designer (MD) will aim to maximize its social welfare by
selecting an optimal set of clients via mechanism $\mathcal{M}$. This is
formulated by:

(9) $\displaystyle\text{maximize}\quad\sum_{i}^{n}u(i)$ satisfy properties (6)
- (8)

To fulfill this goal, the defined properties ensure that each client can
engage in the aggregation process without compromising their energy reserves.
Moreover, the design of the client’s utility function is independent of the
information disclosed to the gateway, such as data quality. This design choice
ensures that clients have no incentive to misreport their values, as doing so
will not adversely affect their utility.

Given the significant resource constraints, such as energy, implementing
mechanism $\mathcal{M}$ with efficiency is paramount. This challenge can be
modeled as a 0/1 knapsack problem, addressed through the dynamic programming
approach (Toth, 1980). This method guarantees an optimal solution. The time
complexity for the dynamic programming solution is $O(N\times W)$, where $N$
is the number of clients and $W$ represents the gateway’s budget constraints.
Dynamic programming resolves complex problems by dividing them into smaller,
overlapping subproblems, addressing each once. For each of the $W$ possible
budgets, we determine the optimal value for every client, resulting in
$N\times W$ subproblems. Each subproblem is solvable in constant time, or
$O(1)$.

###  5.3. Quality-Aware Parameter Aggregation

Upon receiving local updates from the chosen clients, gateways proceed to
aggregate these updates to train new edge models. To achieve high model
accuracy, the strategy employs FedAvg (Shi et al., 2022), focusing on
utilizing high-quality data. This approach involves a weighted parameter
aggregation, where the weighting is determined by the reported data quality of
each client. The formulation of this process is given by:

(10) $w_{new}=\frac{\sum_{i}^{k}\frac{v_{i}}{v_{max}-v_{min}}w_{i}}{k}.$

The weighting of each local parameter $w_{i}$ is determined by
$\frac{v_{i}}{v_{max}-v_{min}}$, positioning the data quality within a $[0,1]$
scale in the overall distribution, with $k$ representing the total number of
participating clients. Aggregation occurs both at the edge server level,
involving gateway and client updates, and at the cloud server level, with
updates from the edge. This method capitalizes on the data quality values
reported by clients to streamline the process. Following aggregation, gateways
forward the updated edge model parameters to the central server, where edge
updates are compiled to construct the global model. During training, local
devices employ a loss function to assess model performance on their datasets,
with gradients informing local updates. The effectiveness of our SusFL system
is gauged through the global model’s performance, as discussed in Section 7.

##  6\. Experimental Setup

Table 1. Dataset Description Metric |  Description
---|---
Serial |  A unique animal identifier
Size-udder |  Size of an udder (udder front left, front right, rear left, and rear right) for inhale and exhale limit
Average temperature |  Average body temperature in Celsius
Hardness |  Hardness of an udder
Pain-level |  Pain due to swelling of an udder
Average-activity |  Average activity recorded by the number of steps taken
Battery-level |  Residual battery life
Timestamp |  Date and time of transmission

###  6.1. Parameterization

This study leverages clinical mastitis data in cows, captured via IoT sensors
on the udder, to detect the disease (Ankitha et al., 2020). The dataset
consists of 6,600 entries, with three records per cow, featuring 15 attributes
monitored by flex and temperature sensors connected to Raspberry Pis and
digital-to-analog converters. Additionally, we utilize data from Virginia
Tech’s SmartFarm Innovation Network (TM), a project of the College of
Agriculture and Life Sciences, which records cow movement activities. This
network serves as a hub for collecting and analyzing data across Virginia
farms, indicating cows typically move at speeds within the range of $[1,2]$
meters per second. Movement probability for cow $i$, denoted as $P_{mv}^{i}$,
is modeled by a normal distribution with an average speed of 1.5 $m/s$ and a
standard deviation of 0.1 $m/s$. Our system, detailed in Table 1, applies FL
to diagnose animal diseases using this data alongside information from
sensors. Additionally, we crafted semi-synthetic datasets by introducing
simulated threats to the original data, following the threat models in Section
4.3.

This work encompasses a farm spanning 40 acres, approximately 160,000 square
meters, with each side measuring 400 meters. It focuses on monitoring 30 cows
using three gateways to ensure efficient surveillance over the 48-hour
simulation period. Each gateway implements an edge model, leveraging our SusFL
mechanism to predict animal diseases, aiming for optimal system performance
given the current conditions. We classify 60% of the cows as clients equipped
with Raspberry Pi (R-pi)-based sensor nodes, with the remaining serving as
standard sensor nodes, as described in Section 4.2. Gateways solicit updates
from these client nodes at 60-minute intervals, designated as $T_{u}$. All
sensor nodes start with a random initial energy level, $E_{init}$, within the
range of $[0.3,0.8)$. Table 2 presents the key design parameters, their
meanings, and default values. Initially, we posit that 30% of the sensors,
denoted as $P_{C}$, are compromised at the system’s onset. The model assumes
full trust in both gateways and the cloud server, with attackers solely
targeting sensor nodes.

Table 2. Key Design Parameters, Meanings, & Default Values Notation |  Meaning |  Value
---|---|---
$n$ |  Total number of sensors(cows) |  30
$N$ |  Total number of clients |  20
$P_{mv}^{1}$ |  Probability of cow $i$ to move |  [0.3,0.7]
$P_{A}$ |  Probability for an attacker or a compromised node to perform a certain attack (e.g., ) |  $0.1$
$P_{C}$ |  Percentage of compromised clients in sensor network |  $0.3$
$T_{s}$ |  Time interval for a sensor to send sensed data |  30 s
$T_{u}$ |  Time interval for a gateway to request local updates |  1 hr
$T_{g}$ |  Time interval for a gateway to report edge models to central server |  1 hr
$E_{init}$ |  Initial energy level of sensors |  $[0.3,0.8)$
$\varepsilon$ |  Threshold for minimum energy level |  0.15
$B$ |  Number of communication rounds budget for each gateway |  5

###  6.2. Metrics

We use the following metrics for performance validation:

  * •

Prediction accuracy evaluates the accuracy of the global model’s predictions
on animal diseases against actual outcomes. Specifically, it assesses the
system’s ability to correctly identify cases of mastitis in cows, calculated
as the proportion of accurate predictions out of the total predictions made
throughout the simulation.

  * •

Energy consumption ($\mathcal{EC}$) quantifies the overall energy usage by the
system for communications and computations within the FL framework. The
$\mathcal{EC}$ is formulated by:

(11) $\mathcal{EC}=\mathcal{COM_{E}}+\mathcal{COMP_{E}},$

where $\mathcal{EC}$, the total energy consumption, comprises two main
components: $\mathcal{COM_{E}}$, the energy used for communications between
clients and gateways, and $\mathcal{COMP_{E}}$, the cumulative energy expended
by sensor nodes for system operations, including model training and energy
depletion over time. The $\mathcal{COM_{E}}$ is calculated by:

(12) $\mathcal{COM}_{E}=\sum_{i=0}^{l}\mathcal{E}_{CR}^{i},$

where $\mathcal{E}_{CR}^{i}$ represents the energy consumed in a single
communication round between a client and a gateway, and $l$ signifies the
total number of communication rounds throughout the simulation. The
$\mathcal{COMP_{E}}$ accounts for the energy utilized in model training and
the natural energy drain experienced during the system’s operational period.
The $\mathcal{COMP_{E}}$ is given by:

(13)
$\displaystyle\mathcal{COMP_{E}}=\mathcal{E}_{TC}+\mathcal{E}_{\mathrm{active}}+\mathcal{E}_{\mathrm{sleep}}$
$\displaystyle=\frac{|S^{*}|e_{TC}}{E_{S}}+\frac{T_{u}}{E_{S}}(d_{\mathrm{active}}+d_{\mathrm{sleep}}),$

where $\mathcal{COMP_{E}}$ accounts for the energy dynamics of the
participating client set $|S^{*}|$, including $e_{TC}$, the energy a client
consumes to join the aggregation process, and $E_{S}$, the full charge energy
level of a sensor. It further considers $d_{\mathrm{active}}$ and
$d_{\mathrm{sleep}}$, the energy depletion rates per second in active and
sleep modes, respectively. $T_{u}$ refers to the interval at which local
updates are requested by the edge server. This model focuses solely on the
energy consumption of sensor nodes, excluding the energy transactions between
gateways and the central server, as they do not face energy limitations.

  * •

Mean Time Between Failures (MTBF) (Colombo and de Bustamante, 1990; Cho et
al., 2019) quantifies the average duration of system operation without
interruptions, calculated as:

(14) $MTBF=\frac{\sum_{f\in F}(u_{s,f}-d_{s,f})}{|F|},$

where $d_{s,f}$ marks the commencement of downtime, $u_{s,f}$ the onset of
uptime, and $F$ the collection of failure instances. A system is considered to
have failed when the average energy level across all nodes falls below
$\varepsilon$.

  * •

Social Welfare encapsulates the collective utility of all sensor clients,
computed as detailed in Eq. (9).

###  6.3. Comparing Schemes

We compare the proposed SusFL against the following state-of-the-art (SOTA) FL
schemes to evaluate its effectiveness:

  * •

FedAvg (T Dinh et al., 2020) employs a variant of the Stochastic Gradient
Descent (SGD) where clients independently execute SGD. The server then derives
global model parameters by averaging these local models, bypassing the need to
aggregate the entire dataset.

  * •

FedProx (Li et al., 2020) modifies FedAvg to better handle client
heterogeneity in computational resources. It incorporates a proximal term into
the local optimization problems, enabling variable local updates and allowing
for the management of statistical heterogeneity across clients.

  * •

FLTrust (Zhang et al., 2020) enhances robustness against data poisoning and
backdoor attacks, minimizing performance impact. It utilizes trusted execution
environments to periodically verify a subset of clients, ensuring integrity.

  * •

DivFL (Balakrishnan et al., 2022) promotes communication efficiency by
introducing a diverse client selection mechanism. Through a greedy algorithm,
it selects an optimal subset of clients for each aggregation round, aiming to
diversify the gradient space and hasten training.

  * •

GreenFL (Kim et al., 2022) introduces a green-quantized FL approach that
incorporates stochastic quantization in both local training and data
transmission. Clients maintain a Quantized Neural Network (QNN) with
adjustable precision levels, addressing the energy-accuracy trade-off through
multi-objective optimization of precision settings.

##  7\. Results and Analyses

To assess the effectiveness of the proposed SusFL scheme alongside the five
existing FL schemes, we conducted 50 simulation runs using the parameter
configurations in Section 6. The results presented for each scheme are the
average outcomes derived from these 50 simulations, ensuring valid evaluation
and comparison.

###  7.1. Comparative Performance Analyses

(a) Prediction Accuracy

(b) Energy Consumption

(c) Social Welfare

(d) MTBF

Figure 4. Comparative performance analysis during training time

Figure 4 showcases the FL training progress across six schemes, as detailed in
Section 6.3, including our proposed SusFL plus the five SOTA schemes. Our
SusFL surpasses the five other schemes in prediction accuracy (Figure 4(a)),
energy consumption (Figure 4(b)), social welfare (Figure 4(c)), and mean time
between failures (MTBF) (Figure 4(d)). This superior performance underscores
the efficiency of SusFL’s quality-aware client selection strategy, which
excludes clients with poor-quality data, thereby enhancing prediction
accuracy. Additionally, by allowing clients to assess their utility for
participation, SusFL achieves minimal energy usage throughout the simulation,
leading to the highest social welfare and MTBF. Notably, SusFL’s energy
consumption is particularly low at the simulation’s start, attributed to the
initial absence of sunlight for charging sensors, indicating a strategic
energy conservation for FL operations. The fluctuation in social welfare
(Figure 4(c)) mirrors the solar pattern, highlighting the dynamic nature of
the smart farm environment. Among the SOTA schemes, DivFL demonstrates the
lowest energy usage due to its efficient client selection method that bolsters
learning effectiveness. Conversely, GreenFL exhibits the highest energy
demand, attributed to the added computational load from managing a QNN. These
findings emphasize the importance of minimizing computational demands on
energy-limited devices in resource-constrained FL systems for smart
environments.

###  7.2. Sensitivity Analyses

####  7.2.1. Effect of Varying Attack Severity ($P_{A}$)

(a) Prediction Accuracy

(b) Energy Consumption

(c) Social Welfare

(d) MTBF

Figure 5. Effect of Varying Attack Severity ($P_{A}$)

Figure 5 illustrates the impact of different levels of attack severity
($P_{A}$) on the performance metrics of FL schemes. An increase in $P_{A}$
results in lower prediction accuracy due to the inclusion of more compromised
sensors, undermining the model’s performance. However, attack severity has
minimal effect on energy metrics, such as energy consumption and MTBF, since
the adversarial strategies do not aim to exhaust client energy or reduce their
operational lifespan.

For SusFL, increased attack severity leads to a decline in social welfare and
MTBF as illustrated in Figure 5(c) and (d). This decline necessitates greater
client involvement in FL tasks to uphold prediction accuracy. Although this
situation slightly raises energy consumption (Figure 5(b)), the increase is
marginal when compared to the gap between SusFL and other SOTA schemes,
rendering the consumption curve nearly flat. Overall, SusFL remains superior
to its peers, such as DivFL and FLProx, in handling varying $P_{A}$,
showcasing robust performance across metrics.

####  7.2.2. Effect of Node Density

(a) Prediction Accuracy

(b) Energy Consumption

(c) Social Welfare

(d) MTBF

Figure 6. Effect of Varying Node Density

Figure 6 investigates the performance metrics’ response to the varying number
of client sensor nodes within the network. The findings demonstrate SusFL’s
adeptness at managing FL operations across expansive sensor networks. A
notable observation is the improvement in prediction accuracy with SusFL,
attributed to a broader distribution of clients across the farm. This
dispersion increases the likelihood of each client possessing high-quality
data, thereby providing gateways with more selection options to maintain high
accuracy. Consequently, this strategic client selection leads to reduced
average energy consumption (Figure 6(b)), enhancing social welfare (Figure
6(c)) and MTBF (Figure 6(d)).

Contrastingly, the performance of other schemes appears relatively unaffected
by changes in node density across all metrics, with the exception of GreenFL’s
prediction accuracy. As the number of clients grows, GreenFL’s prediction
accuracy diminishes, suggesting a potential shortfall in managing clients with
suboptimal local models. This highlights SusFL’s superiority in optimizing
both the quality and efficiency of FL operations in large-scale sensor
networks.

####  7.2.3. Effect of Initial Energy Levels ($E_{init}$) on clients

(a) Prediction Accuracy

(b) Energy Consumption

(c) Social Welfare

(d) MTBF

Figure 7. Effect of Varying Initial Energy Levels ($E_{init}$)

Figure 6 explores how varying initial energy levels in client sensor nodes
influence performance metrics. The results indicate that SusFL surpasses the
considered SOTA schemes in prediction accuracy as the clients’ energy levels
rise. This advantage stems from SusFL’s client selection strategy,
prioritizing clients based on their energy availability. Consequently,
increasing initial energy allows more clients to engage in FL operations,
enhancing prediction accuracy. In contrast, since the number of clients
involved in aggregation remains constant in other schemes, their prediction
accuracy remains largely unaffected by changes in energy availability.

Furthermore, the presence of more energy in the system, despite causing a
slight uptick in energy consumption for the FL process (Figure 7(b)), leads to
improved social welfare and MTBF as indicated in Figure 7(c) and (d). This
pattern is unique to SusFL as the fixed participation of clients in the FL
process does not adjust to the available system energy, highlighting SusFL’s
adaptability and efficiency in utilizing energy resources for optimal FL
operations.

##  8\. Conclusion & Future work

From our research, we highlight the following key findings in the performance
of SusFL:

  1. (1)

Enhanced energy efficiency and prediction accuracy: SusFL demonstrates
superior efficiency in global model training within FL operations, utilizing
the least amount of energy to attain the highest prediction accuracy compared
to benchmark schemes. This efficiency is attributed to an energy-aware client
selection mechanism that adeptly chooses an optimal set of clients, thereby
balancing high accuracy with energy conservation in the sensor network.

  2. (2)

Hightened System availability and MTBF: SusFL excels in MTBF, enhancing system
reliability amid energy variability and environmental dynamics in smart
farming. This finding emphasizes the scheme’s robustness and the necessity to
minimize computational demands on sensor nodes, a lesson underscored by
GreenFL’s relative underperformance.

  3. (3)

Improved resilience against attacks: In scenarios involving cyber and
adversarial attacks, SusFL stands out for its ability to maintain high
prediction accuracy with minimal energy use, thereby ensuring ongoing system
availability and sustainability. This resilience highlights SusFL’s
effectiveness in safeguarding FL operations against potential security
threats.

These insights underscore SusFL’s comprehensive approach to optimizing FL for
energy-constrained environments, offering a scalable, secure, and efficient
solution for smart agricultural practices.

For future work, we will enhance our study in several key areas:

  * •

Fairness and privacy: Incorporate fairness and privacy preservation into
client selection to ensure secure and equitable participation in the FL
process.

  * •

Scalability enhancement: Enhance scalability by increasing the number of
clients in the FL operations, expanding the system’s capacity for larger and
more diverse datasets.

  * •

Performance optimization: Optimize performance by refining the client
selection mechanism to improve prediction accuracy and energy efficiency,
especially for large-scale operations.

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