This
paper
presents
the
use
of
federated
learning
(FL)
in
healthcare
to
improve
efficiency
and
accuracy
medical
diagnosis
while
addressing
privacy
concerns
related
data.
FL
allows
data
remain
local
trains
models
independently,
with
only
model
parameters
communicated
server.
Creating
is
a
popular
solution
systems
now,
particularly
increasing
Internet
Medical
Things
(IoMT)
devices
that
enable
storage
large
amounts
health
work
provides
comprehensive
analysis
current
employed
various
applications
healthcare.
We
applied
skin
cancer
set
achieved
remarkable
result
classification
90%
or
higher,
demonstrating
potential
image
tasks.
In
this
context,
we
also
discuss
bottlenecks
future
research
directions
IEEE/CAA Journal of Automatica Sinica,
Год журнала:
2024,
Номер
11(4), С. 824 - 850
Опубликована: Март 20, 2024
When
data
privacy
is
imposed
as
a
necessity,
Federated
learning
(FL)
emerges
relevant
artificial
intelligence
field
for
developing
machine
(ML)
models
in
distributed
and
decentralized
environment.
FL
allows
ML
to
be
trained
on
local
devices
without
any
need
centralized
transfer,
thereby
reducing
both
the
exposure
of
sensitive
possibility
interception
by
malicious
third
parties.
This
paradigm
has
gained
momentum
last
few
years,
spurred
plethora
real-world
applications
that
have
leveraged
its
ability
improve
efficiency
accommodate
numerous
participants
with
their
sources.
By
virtue
FL,
can
learned
from
all
such
sources
while
preserving
privacy.
The
aim
this
paper
provide
practical
tutorial
including
short
methodology
systematic
analysis
existing
software
frameworks.
Furthermore,
our
provides
exemplary
cases
study
three
complementary
perspectives:
i)
Foundations
describing
main
components
key
elements
categories;
ii)
Implementation
guidelines
study,
systematically
examining
functionalities
provided
frameworks
deployment,
devising
design
scenario,
providing
source
code
different
approaches;
iii)
Trends,
shortly
reviewing
non-exhaustive
list
research
directions
are
under
active
investigation
current
landscape.
ultimate
purpose
work
establish
itself
referential
researchers,
developers,
scientists
willing
explore
capabilities
applications.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 53881 - 53899
Опубликована: Янв. 1, 2024
Machine
learning
is
a
powerful
technology
for
extracting
information
from
data
of
diverse
nature
and
origin.
As
its
deployment
increasingly
depends
on
multiple
entities,
ensuring
privacy
these
contributors
becomes
paramount
the
integrity
fairness
machine
endeavors.
This
review
looks
into
recent
advancements
in
secure
multi-party
computation
(SMPC)
learning,
pivotal
championing
privacy.
We
evaluate
applications
various
aspects,
including
security
models,
requirements,
system
types,
service
aligning
with
IEEE's
recommended
practices
SMPC.
Broadly,
SMPC
systems
are
divided
two
categories:
homomorphic-based
systems,
which
facilitate
computations
encrypted
data,
remains
confidential,
secret
sharing-based
disseminate
across
parties
fragmented
shares.
Our
literature
analysis
highlights
certain
gaps,
such
as
requisites,
streamlined
exchange,
incentive
structures,
authenticity,
operational
efficiency.
Recognizing
challenges
lead
to
envisioning
holistic
protocol
tailored
applications.
Artificial Intelligence Review,
Год журнала:
2025,
Номер
58(3)
Опубликована: Янв. 13, 2025
Federated
Learning
(FL)
is
a
technique
that
can
learn
global
machine-learning
model
at
central
server
by
aggregating
locally
trained
models.
This
distributed
approach
preserves
the
privacy
of
local
However,
FL
systems
are
inherently
vulnerable
to
significant
security
challenges
such
as
cyber-attacks,
handling
non-independent
and
identically
(non-IID)
data,
data
concerns.
systematic
literature
review
addresses
these
issues
examining
advanced
neural
network
models,
feature
engineering
methods,
privacy-preserving
techniques
within
intrusion
detection
(IDS)
for
environments.
These
key
elements
improving
systems.
To
best
our
knowledge,
this
among
first
comprehensively
explore
combined
impacts
technologies.
We
analyzed
88
studies
published
between
2021
October
2024.
study
offers
valuable
insights
future
research
directions,
including
scaling
in
real-world
environment.
Journal of Education for Library and Information Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 24, 2025
The
rapid
advancement
of
technology
has
presented
marginalized
communities
in
libraries
with
numerous
privacy
and
security
challenges.
Many
researchers
have
emphasized
the
importance
Privacy-Enhancing
Technologies
(PETs),
suggesting
that
these
technical
solutions
can
effectively
assist
users
safeguarding
their
personally
identifiable
information.
This
qualitative
research
project
conducted
14
semi-structured
interviews
US-based,
LGBTQ+
Library
Information
Science
(LIS)
students
aiming
to
explore
motivations,
challenges,
criteria
for
PET
usage.
results
revealed
future
LIS
professionals
commonly
utilize
two-factor
authentication
ad-blocker
software
protect
online
identity
locations.
Participants
also
experienced
significant
challenges
using
PETs,
such
as
high
costs,
limited
educational
awareness
about
existence
utility,
difficulties
understanding
how
them.
Journal of Telecommunications and Information Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 7, 2025
The
integration
of
machine
learning
in
biomedical
engineering
applications
is
crucial
to
ensure
user
data
security
and
privacy.
This
work
explores
anonymization
differential
privacy
(DP)
frameworks
reduce
the
risk
biometric
identification.
DP
method
used
train
models
biosignal
without
compromising
diagnostic
results.
proposed
approach
for
privacy-preserving
arrhythmia
detection
uses
a
system
that
reduces
discrepancies
between
prepossessed
raw
data,
maintaining
correct
level
precision
while
improving
application
evaluated
using
control
model
analyze
accuracy
difference
when
input
data.
