Blockchains,
Journal Year:
2025,
Volume and Issue:
3(1), P. 1 - 1
Published: Jan. 1, 2025
Federated
learning
(FL)
has
emerged
as
an
efficient
machine
(ML)
method
with
crucial
privacy
protection
features.
It
is
adapted
for
training
models
in
Internet
of
Things
(IoT)-related
domains,
including
smart
healthcare
systems
(SHSs),
where
the
introduction
IoT
devices
and
technologies
can
arise
various
security
concerns.
However,
FL
cannot
solely
address
all
challenges,
privacy-enhancing
(PETs)
blockchain
are
often
integrated
to
enhance
frameworks
within
SHSs.
The
critical
questions
remain
regarding
how
these
they
contribute
enhancing
This
survey
addresses
by
investigating
recent
advancements
on
combination
PETs
healthcare.
First,
this
emphasizes
integration
into
context.
Second,
challenge
integrating
FL,
it
examines
three
main
technical
dimensions
such
blockchain-enabled
model
storage,
aggregation,
gradient
upload
frameworks.
further
explores
collectively
ensure
integrity
confidentiality
data,
highlighting
their
significance
building
a
trustworthy
SHS
that
safeguards
sensitive
patient
information.
The Journal of Supercomputing,
Journal Year:
2024,
Volume and Issue:
81(1)
Published: Dec. 10, 2024
The
integration
of
artificial
intelligence
(AI)
has
caused
information
and
communication
technology
(ICT)
to
undergo
a
number
recent
rapid
fluctuations.
These
changes
have
primarily
affected
the
areas
management,
end-to-end
device
interconnectivity,
resource
organization,
communication,
networking,
application-related
aspects
ICT.
Owing
complex
structure
applicational
connectedness,
evaluating
each
aforementioned
opportunities
concurrently
reflects
idea
heterogeneity.
association
multiple
end
devices,
particularly
in
interoperable
space,
integrity,
privacy
protection,
security,
provenance,
massive
volume
everyday
media
data
generated
modern
healthcare
setting
could
also
provide
significant
issues.
To
address
these
issues,
decentralized,
secure,
economical
optimization,
intelligent
network
activities
organization
are
necessary.
Blockchain
plays
crucial
role
providing
distributed
storage
sharing,
exchange
for
automated
decision-making,
privacy,
security
AI-enabled
machine
learning
(ML)
models.
However,
models—support
vector
machine,
particular—have
impact
on
growth
consortium
networks
among
connected
nodes,
resolving
issues
with
scalability,
processing.
By
three
main
problems
seamless
peer-to-peer
between
infrastructure
we
novel
technique
this
proposed
architecture.
approach
is
unique,
as
demonstrated
by
simulation-based
results,
which
display
huge
differences
1.37%,
1.56%,
1.87%,
respectively.
background
evaluation
consists
following
areas:
(i)
protect
decision-making;
(ii)
integrity
smooth
sharing
exchange;
(iii)
optimization
enable
across
heterogeneous
devices.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(9)
Published: Aug. 8, 2024
Abstract
The
fusion
of
blockchain
and
artificial
intelligence
(AI)
marks
a
paradigm
shift
in
healthcare,
addressing
critical
challenges
securing
electronic
health
records
(EHRs),
ensuring
data
privacy,
facilitating
secure
transmission.
This
study
provides
comprehensive
analysis
the
adoption
AI
within
spotlighting
their
role
fortifying
security
transparency
leading
trajectory
for
promising
future
realm
healthcare.
Our
study,
employing
PRISMA
model,
scrutinized
402
relevant
articles,
narrative
to
explore
review
includes
architecture
blockchain,
examines
applications
with
without
integration,
elucidates
interdependency
between
blockchain.
major
findings
include:
(i)
it
protects
transfer,
digital
records,
security;
(ii)
enhances
EHR
COVID-19
transmission,
thereby
bolstering
healthcare
efficiency
reliability
through
precise
assessment
metrics;
(iii)
addresses
like
security,
decentralized
computing,
forming
robust
tripod.
revolutionize
by
EHRs,
enhancing
security.
Private
reflects
sector’s
commitment
improved
accessibility.
convergence
promises
enhanced
disease
identification,
response,
overall
efficacy,
key
sector
challenges.
Further
exploration
advanced
features
integrated
enhance
outcomes,
shaping
global
delivery
guaranteed
innovation.
Healthcare,
Journal Year:
2024,
Volume and Issue:
12(24), P. 2587 - 2587
Published: Dec. 22, 2024
Federated
learning
(FL)
is
revolutionizing
healthcare
by
enabling
collaborative
machine
across
institutions
while
preserving
patient
privacy
and
meeting
regulatory
standards.
This
review
delves
into
FL's
applications
within
smart
health
systems,
particularly
its
integration
with
IoT
devices,
wearables,
remote
monitoring,
which
empower
real-time,
decentralized
data
processing
for
predictive
analytics
personalized
care.
It
addresses
key
challenges,
including
security
risks
like
adversarial
attacks,
poisoning,
model
inversion.
Additionally,
it
covers
issues
related
to
heterogeneity,
scalability,
system
interoperability.
Alongside
these,
the
highlights
emerging
privacy-preserving
solutions,
such
as
differential
secure
multiparty
computation,
critical
overcoming
limitations.
Successfully
addressing
these
hurdles
essential
enhancing
efficiency,
accuracy,
broader
adoption
in
healthcare.
Ultimately,
FL
offers
transformative
potential
secure,
data-driven
promising
improved
outcomes,
operational
sovereignty
ecosystem.
In
the
ever-evolving
healthcare
sector,
widespread
adoption
of
Internet
Things
and
wearable
technologies
facilitates
remote
patient
monitoring.
However,
existing
client/server
infrastructure
poses
significant
security
privacy
challenges,
necessitating
strict
adherence
to
data
regulations.
To
combat
these
issues,
a
decentralized
approach
is
imperative,
blockchain
technology
emerges
as
compelling
solution
for
strengthening
medical
systems
security.
This
paper
introduces
HNMblock,
model
that
elevates
realms
epidemiological
monitoring,
system
security,
wellness
enhancement.
By
harnessing
transparency
immutability
inherent
in
blockchain,
HNMblock
empowers
real-time,
tamper-proof
tracking
data,
enabling
swift
responses
disease
outbreaks.
Furthermore,
it
fortifies
through
advanced
cryptographic
techniques
smart
contracts,
with
paramount
focus
on
safeguarding
privacy.
also
fosters
personalized
health
care,
encouraging
involvement
data-informed
decision-making.
The
integration
within
domain,
exemplified
by
holds
potential
revolutionize
management,
surveillance,
wellness,
meticulously
explored
this
research
article.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 155 - 186
Published: March 7, 2025
Federated
learning
is
an
emerging
powerful
approach
for
training
ML
models
across
decentralized
data
sources,
such
as
in
healthcare,
without
actually
transferring
the
sensitive
to
a
central
server.
Although
FL
naturally
reduces
risks
of
privacy
since
are
confined
local
devices,
it
still
can
be
highly
vulnerable
sophisticated
attacks
model
inversion,
poisoning,
and
inference
attacks.
Advanced
privacy-preserving
techniques
that
nowadays
studied
addressing
security
concerns
this
paper
include
differential
privacy,
homomorphic
encryption,
secure
aggregation.
These,
therefore,
open
room
adopted
solution
some
privacy-sensitive
domains.
This
conducts
thorough
review
discussed
outlines
customized
framework
healthcare
applications
allow
patient
safeguarded
detrimental
effects
on
performance
accuracy.
Conclusion:
Sensors,
Journal Year:
2025,
Volume and Issue:
25(9), P. 2795 - 2795
Published: April 29, 2025
Internet
of
Medical
Things
(IoMT)
is
a
rapidly
evolving
field
with
the
potential
to
bring
significant
changes
healthcare.
While
several
surveys
have
examined
structure
and
operation
these
systems,
critical
aspects
such
as
interoperability,
sustainability,
security,
runtime
self-adaptation,
configurability
are
sometimes
overlooked.
Interoperability
essential
for
integrating
data
from
various
devices
platforms
provide
comprehensive
view
patient's
health.
Sustainability
addresses
environmental
impact
IoMT
technologies,
crucial
in
context
green
computing.
Security
ensures
protection
sensitive
patient
breaches
manipulation.
Runtime
self-adaptation
allows
systems
adjust
changing
conditions
environments.
Configurability
enables
frameworks
monitor
diverse
manage
different
treatment
paths.
This
article
reviews
current
techniques
addressing
highlights
areas
requiring
further
research.
Future Internet,
Journal Year:
2024,
Volume and Issue:
17(1), P. 2 - 2
Published: Dec. 25, 2024
The
development
of
medical
data
and
resources
has
become
essential
for
enhancing
patient
outcomes
operational
efficiency
in
an
age
when
digital
innovation
healthcare
is
becoming
more
important.
rapid
growth
the
Internet
Medical
Things
(IoMT)
changing
management,
but
it
also
brings
serious
issues
like
privacy,
malicious
attacks,
service
quality.
In
this
study,
we
present
EdgeGuard,
a
novel
decentralized
architecture
that
combines
blockchain
technology,
federated
learning,
edge
computing
to
address
those
challenges
coordinate
across
IoMT
networks.
EdgeGuard
uses
privacy-preserving
learning
approach
keep
sensitive
local
promote
collaborative
model
training,
solving
issues.
To
prevent
modification
unauthorized
access,
blockchain-based
access
control
integrity
verification
system.
improve
system
scalability
by
offloading
computational
tasks
from
devices
with
limited
resources.
We
have
made
several
technological
advances,
including
lightweight
consensus
mechanism
designed
networks,
adaptive
resource
allocation
method
based
on
reinforcement
algorithm
optimized
differential
privacy.
create
smart
contracts
secure
multi-party
protocol
updates.
outperforms
existing
solutions
terms
performance,
value,
privacy
protection
wide
range
real-world
datasets.
This
work
enhances
safe,
effective,
management
ecosystems
while
maintaining
outstanding
standards
security
efficiency,
enabling
large-scale
healthcare.
