Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions
Agriculture,
Journal Year:
2025,
Volume and Issue:
15(9), P. 934 - 934
Published: April 25, 2025
The
advancement
of
precision
agriculture
increasingly
depends
on
innovative
technological
solutions
that
optimize
resource
utilization
and
minimize
environmental
impact.
This
paper
introduces
a
novel
heterogeneous
federated
learning
architecture
specifically
designed
for
intelligent
agricultural
systems,
with
focus
combine
tractors
equipped
advanced
nutrient
crop
health
sensors.
Unlike
conventional
FL
applications,
our
uniquely
addresses
the
challenges
communication
efficiency,
dynamic
network
conditions,
allocation
in
rural
farming
environments.
By
adopting
decentralized
approach,
we
ensure
sensitive
data
remain
localized,
thereby
enhancing
security
while
facilitating
effective
collaboration
among
devices.
promotes
formation
adaptive
clusters
based
operational
capabilities
geographical
proximity,
optimizing
between
edge
devices
global
server.
Furthermore,
implement
robust
checkpointing
mechanism
transmission
strategy,
ensuring
efficient
model
updates
face
fluctuating
conditions.
Through
comprehensive
assessment
computational
power,
energy
latency,
system
intelligently
classifies
devices,
significantly
overall
efficiency
processes.
details
architecture,
procedures,
evaluation
methodologies,
demonstrating
how
approach
has
potential
to
transform
practices
through
data-driven
decision-making
promote
sustainable
tailored
unique
sector.
Language: Английский
Ethical and Secure AI Applications in Agricultural Research
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 43 - 76
Published: May 14, 2025
The
integration
of
Artificial
Intelligence
(AI)
into
agricultural
research
has
introduced
transformative
innovations
that
promise
increased
productivity,
sustainability,
and
precision
in
farming
practices.
However,
the
rapid
advancement
deployment
AI
systems
this
domain
also
bring
significant
ethical
security
concerns.
Issues
such
as
data
privacy,
algorithmic
bias,
equitable
access,
environmental
impacts,
transparency
present
complex
challenges
must
be
carefully
navigated.
This
chapter
explores
critical
considerations
risks
associated
with
applications
research,
while
highlighting
immense
opportunities
for
advancing
scientific
understanding,
improving
food
security,
promoting
sustainable
By
examining
current
trends,
case
studies,
regulatory
frameworks,
provides
a
comprehensive
overview
how
can
ethically
securely
developed
deployed,
ensuring
innovation
serves
both
humanity
environment
responsibly.
Language: Английский