e-Prime - Advances in Electrical Engineering Electronics and Energy,
Journal Year:
2024,
Volume and Issue:
8, P. 100522 - 100522
Published: March 25, 2024
Machine
learning
is
the
analysis
based
on
data
that
gives
strategic
decisions
to
cultivate
an
accurate
and
stable
framework
for
different
applications.
Access
medical
with
utmost
privacy
high
rates
still
a
challenging
problem.
To
accomplish
above-mentioned
features,
performance
of
federated
(FL)
5G
massive
multiple-input-multiple-output
(MIMO)
investigated
IoMT
systems.
This
provides
energy-efficient
privacy-preserving
solution
throughput
digital
health
system.
In
proposed
model,
uplink
scenario
using
detection
techniques.
The
are
evaluated
at
central
server
edge
devices
signal-to-noise
ratios
(SNRs)
fading
channels.
ML
bit
error
rate
(BER)
better
than
MRC
but
higher
complexity.
accuracy
obtained
approximately
90%
improvement
around
8%
9%
as
compared
baseline
approach.
IEEE Internet of Things Journal,
Journal Year:
2024,
Volume and Issue:
11(13), P. 23862 - 23877
Published: April 12, 2024
Federated
Learning
(FL)
allows
clients
to
keep
local
datasets
and
train
collaboratively
by
uploading
model
gradients,
which
achieves
the
goal
of
learning
from
fragmented
sensitive
data.
Although
FL
prevents
clients'
being
shared
directly,
private
information
may
be
leaked
through
gradients.
To
mitigate
this
problem,
we
combine
game
theory
design
an
scheme
(IMFL)
based
on
incentive
mechanism
differential
privacy
(DP).
Firstly,
explore
three
DP
variants,
all
are
resistant
deep
leakage
gradients
(DLG)
but
differ
in
their
level
protection.
In
addition,
perform
convergence
analysis
DP.
Then,
with
assistance
theory,
analyze
natural
state
server
process
formulate
utility
function
both
sides
under
case
considering
attack.
Finally,
establish
optimization
problem
as
a
Stackelberg
solve
for
optimal
strategy
deriving
Nash
equilibrium
achieve
personalized
Theoretical
proof
demonstrates
that
types
entities
can
actions
maximizing
functions
upon
reaching
equilibrium.
Besides,
extensive
experiments
conducted
real-world
demonstrate
IMFL
is
efficient
feasible.
IEEE Open Journal of the Communications Society,
Journal Year:
2024,
Volume and Issue:
5, P. 2926 - 2941
Published: Jan. 1, 2024
Towards
6G,
a
key
challenge
lies
in
the
placement
of
virtual
network
functions
on
physical
resources.
This
becomes
complex
due
to
dynamic
nature
mobile
environments,
making
design
major
point
research.
We
propose
framework
that
sees
this
as
and
collective
process,
presenting
novel
perspective
which
encompasses
transport
wireless
segment
aspects.
The
is
built
around
an
analytical
modeling
algorithmic
tools
rely
systems'
paradigm
multiplex
networks
evolutionary
game
theory.
enables
capturing
layered
heterogeneous
environment.
Evolutionary
theory
models
dynamical
behavior
system
social
where
each
decision
influences
overall
outcome.
Our
model
allows
us
achieve
scheme
optimizes
6G
deployment
minimizes
number
active
computational
nodes.
Compared
traditional
centric
approach,
it
effectively
reduces
interference,
ensuring
network's
effective
operation
performance.
Results
show
efficacy
strategy,
enabling
distribution
outcome
dilemma,
highlight
potential
applicability
approach
tackle
function
problem
networks.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 93060 - 93074
Published: Jan. 1, 2024
Edge
Computing
provides
an
effective
solution
for
relieving
IoT
devices
from
the
burden
of
handling
Machine
Learning
(ML)
tasks.
Further,
given
limited
storage
capacity
these
devices,
they
can
only
accommodate
a
restricted
amount
data
training,
resulting
in
higher
error
rates
ML
predictions.
To
address
this
limitation,
leverage
and
collaborate
learning
process
through
designated
peer
acting
as
device.
However,
transmission
offloaded
tasks
over
wireless
access
network
poses
challenges
terms
time
energy
consumption.
Consequently,
although
collaborative
diminish
variance
learned
model,
it
introduces
communication
cost,
dependent
on
chosen
In
light
considerations,
paper
coalition
formation
game
that
proposes
distributed
Federated
approach,
where
autonomously
efficiently
select
most
suitable
device,
aiming
to
minimize
both
their
cost.
IEEE Internet of Things Journal,
Journal Year:
2024,
Volume and Issue:
11(10), P. 18463 - 18482
Published: Feb. 12, 2024
Federated
learning
is
envisioned
to
be
a
key
enabler
of
network
functionalities
based
on
artificial
intelligence.
Multiple
access
mechanisms
supporting
the
task
must
then
designed,
in
order
provide
an
efficient
interplay
between
communication
and
computation
resources.
This
work
considers
thus
multi-level
slotted
random
scheme
autonomously
optimised
by
each
node.
Due
their
mutual
coupling,
nodes'
interaction
instance
Best
Response
Dynamics
(BRD)
Generalised
Nash
Equilibrium
Problem
(GNEP).
Within
this
framework,
levers
are
identified,
guaranteeing
convergence
interactions
equilibrium
point
at
which
federated
supported.
These
levers,
manager
can
act,
validated
numerical
simulations.
latter
moreover
show
that
performance
loss
due
autonomous
character
nodes
negligible
with
respect
result
centralised
optimisation.
On
broader
mathematical
level,
defines
class
GNEPs
for
sufficient
conditions
totally
asynchronous
BRD
obtained.
The
considered
class,
named
polyhedral
strategy
sets
variable
right-hand
sides,
encompasses
wide
variety
GNEPs,
particular
neither
jointly
convex
nor
generalised
potential
games.
obtained
depend
first
second
derivatives
objective
constraint
functions,
they
constitute
off-the-shelf
framework
study
belonging
identified
class.
