Diagnostics,
Journal Year:
2023,
Volume and Issue:
13(19), P. 3140 - 3140
Published: Oct. 6, 2023
Since
its
introduction
in
2016,
researchers
have
applied
the
idea
of
Federated
Learning
(FL)
to
several
domains
ranging
from
edge
computing
banking.
The
technique’s
inherent
security
benefits,
privacy-preserving
capabilities,
ease
scalability,
and
ability
transcend
data
biases
motivated
use
this
tool
on
healthcare
datasets.
While
reviews
exist
detailing
FL
applications,
review
focuses
solely
different
applications
medical
imaging
datasets,
grouping
by
diseases,
modality,
and/or
part
body.
This
Systematic
Literature
was
conducted
querying
consolidating
results
ArXiv,
IEEE
Xplorer,
PubMed.
Furthermore,
we
provide
a
detailed
description
architecture,
models,
descriptions
performance
achieved
how
compare
with
traditional
Machine
(ML)
models.
Additionally,
discuss
highlighting
two
primary
forms
techniques,
including
homomorphic
encryption
differential
privacy.
Finally,
some
background
information
context
regarding
where
contributions
lie.
is
organized
into
following
categories:
architecture/setup
type,
data-related
topics,
security,
learning
types.
progress
has
been
made
within
field
imaging,
much
room
for
improvement
understanding
remains,
an
emphasis
issues
remaining
concerns
researchers.
Therefore,
improvements
are
constantly
pushing
forward.
highlighted
challenges
deploying
provided
recommendations
future
directions.
Applied Sciences,
Journal Year:
2022,
Volume and Issue:
12(23), P. 12080 - 12080
Published: Nov. 25, 2022
Heart
disease
is
one
of
the
lethal
diseases
causing
millions
fatalities
every
year.
The
Internet
Medical
Things
(IoMT)
based
healthcare
effectively
enables
a
reduction
in
death
rate
by
early
diagnosis
and
detection
disease.
biomedical
data
collected
using
IoMT
contains
personalized
information
about
patient
this
has
serious
privacy
concerns.
To
overcome
issues,
several
protection
laws
are
proposed
internationally.
These
created
huge
problem
for
techniques
used
traditional
machine
learning.
We
propose
framework
on
federated
matched
averaging
with
modified
Artificial
Bee
Colony
(M-ABC)
optimization
algorithm
to
issues
improve
method
prediction
heart
paper.
technique
improves
accuracy,
classification
error,
communication
efficiency
as
compared
state-of-the-art
learning
algorithms
real-world
dataset.
Big Data and Cognitive Computing,
Journal Year:
2022,
Volume and Issue:
6(4), P. 127 - 127
Published: Oct. 26, 2022
Federated
learning
(FL)
is
one
of
the
leading
paradigms
modern
times
with
higher
privacy
guarantees
than
any
other
digital
solution.
Since
its
inception
in
2016,
FL
has
been
rigorously
investigated
from
multiple
perspectives.
Some
these
perspectives
are
extensions
FL’s
applications
different
sectors,
communication
overheads,
statistical
heterogeneity
problems,
client
dropout
issues,
legitimacy
system
results,
preservation,
etc.
Recently,
being
increasingly
used
medical
domain
for
purposes,
and
many
successful
exist
that
serving
mankind
various
ways.
In
this
work,
we
describe
novel
challenges
paradigm
special
emphasis
on
COVID-19
pandemic.
We
synergies
emerging
technologies
to
accomplish
services
fight
analyze
recent
open-source
development
which
can
help
designing
scalable
reliable
models.
Lastly,
suggest
valuable
recommendations
enhance
technical
persuasiveness
paradigm.
To
best
authors’
knowledge,
first
work
highlights
efficacy
era
COVID-19.
The
analysis
enclosed
article
pave
way
understanding
field,
specifically
Diagnostics,
Journal Year:
2023,
Volume and Issue:
13(19), P. 3140 - 3140
Published: Oct. 6, 2023
Since
its
introduction
in
2016,
researchers
have
applied
the
idea
of
Federated
Learning
(FL)
to
several
domains
ranging
from
edge
computing
banking.
The
technique’s
inherent
security
benefits,
privacy-preserving
capabilities,
ease
scalability,
and
ability
transcend
data
biases
motivated
use
this
tool
on
healthcare
datasets.
While
reviews
exist
detailing
FL
applications,
review
focuses
solely
different
applications
medical
imaging
datasets,
grouping
by
diseases,
modality,
and/or
part
body.
This
Systematic
Literature
was
conducted
querying
consolidating
results
ArXiv,
IEEE
Xplorer,
PubMed.
Furthermore,
we
provide
a
detailed
description
architecture,
models,
descriptions
performance
achieved
how
compare
with
traditional
Machine
(ML)
models.
Additionally,
discuss
highlighting
two
primary
forms
techniques,
including
homomorphic
encryption
differential
privacy.
Finally,
some
background
information
context
regarding
where
contributions
lie.
is
organized
into
following
categories:
architecture/setup
type,
data-related
topics,
security,
learning
types.
progress
has
been
made
within
field
imaging,
much
room
for
improvement
understanding
remains,
an
emphasis
issues
remaining
concerns
researchers.
Therefore,
improvements
are
constantly
pushing
forward.
highlighted
challenges
deploying
provided
recommendations
future
directions.
