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y
-
p
r
eser
v
in
g
,
an
d
s
ca
lab
le
m
o
n
ito
r
in
g
f
r
am
ew
o
r
k
s
is
cr
u
cial
in
en
s
u
r
in
g
th
at
s
en
s
itiv
e
m
ed
ical
d
ata
i
s
s
ec
u
r
ely
p
r
o
ce
s
s
ed
wh
ile
m
ain
tain
in
g
h
ig
h
d
iag
n
o
s
tic
ac
cu
r
ac
y
.
Fed
er
ated
lear
n
in
g
(
FL)
h
as
em
er
g
ed
as
a
p
r
o
m
is
in
g
s
o
lu
ti
o
n
t
o
t
h
ese
ch
allen
g
es
b
y
allo
win
g
d
ec
en
tr
alize
d
d
ata
p
r
o
ce
s
s
in
g
with
o
u
t
co
m
p
r
o
m
is
in
g
p
atien
t
p
r
iv
ac
y
.
B
y
d
i
s
tr
ib
u
tin
g
m
o
d
el
tr
ain
in
g
ac
r
o
s
s
m
u
ltip
le
ed
g
e
d
ev
ices
an
d
ag
g
r
eg
atin
g
r
esu
lt
s
in
a
s
ec
u
r
e
m
a
n
n
er
,
FL
en
s
u
r
es
th
at
p
atien
t
d
ata
r
em
ain
s
l
o
ca
l
wh
ile
en
a
b
lin
g
co
llectiv
e
in
tellig
en
ce
f
o
r
im
p
r
o
v
ed
p
r
ed
ictio
n
an
d
d
iag
n
o
s
is
.
Mu
lti
-
task
tr
an
s
f
er
lear
n
in
g
(
MT
T
L
)
f
u
r
th
er
e
n
h
an
ce
s
th
is
ap
p
r
o
ac
h
b
y
lev
er
a
g
in
g
k
n
o
w
led
g
e
f
r
o
m
o
n
e
d
o
m
ain
t
o
im
p
r
o
v
e
lear
n
in
g
in
r
elate
d
d
o
m
ain
s
[
3
]
,
[
4
]
.
I
n
th
e
co
n
te
x
t
o
f
r
em
o
te
h
ea
lth
m
o
n
ito
r
in
g
,
tr
an
s
f
er
lear
n
in
g
ca
n
h
elp
a
d
ap
t
p
r
e
-
tr
ai
n
ed
m
o
d
els
f
o
r
d
if
f
er
en
t
b
u
t
r
elate
d
task
s
s
u
ch
as
ar
r
h
y
th
m
ia
d
etec
tio
n
,
v
ital
s
ig
n
m
o
n
ito
r
in
g
,
an
d
ac
tiv
ity
r
ec
o
g
n
itio
n
.
T
h
is
ap
p
r
o
ac
h
r
ed
u
ce
s
th
e
n
ee
d
f
o
r
e
x
ten
s
iv
e
lab
eled
d
ata,
m
ak
in
g
it
p
a
r
ticu
lar
ly
b
e
n
ef
icial
f
o
r
r
ea
l
-
w
o
r
l
d
h
ea
lth
ca
r
e
ap
p
licatio
n
s
w
h
e
r
e
lab
eled
m
ed
ical
d
atasets
ar
e
o
f
ten
lim
ited
[
5
]
.
T
h
is
p
ap
er
p
r
o
p
o
s
es
a
h
ier
ar
ch
ical
f
ed
er
ated
m
u
lti
-
task
tr
an
s
f
er
lear
n
in
g
f
r
am
ewo
r
k
t
h
at
in
teg
r
ates
th
ese
cu
ttin
g
-
ed
g
e
m
eth
o
d
o
l
o
g
ies
to
cr
ea
te
a
r
o
b
u
s
t,
r
ea
l
-
ti
m
e
r
em
o
te
p
atien
t
m
o
n
ito
r
in
g
s
y
s
tem
.
T
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
aim
s
to
ac
h
iev
e
th
r
ee
k
ey
o
b
jectiv
es:
−
Acc
u
r
ate
d
etec
tio
n
o
f
p
ar
o
x
y
s
m
al
ar
r
h
y
th
m
ia
:
Usi
n
g
ad
v
a
n
ce
d
s
ig
n
al
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
f
o
r
R
-
p
ea
k
d
etec
tio
n
an
d
QR
in
ter
v
al
d
elin
ea
tio
n
,
th
e
s
y
s
tem
ca
n
id
en
tify
an
d
class
if
y
d
if
f
er
en
t
ty
p
es
o
f
p
ar
o
x
y
s
m
al
ar
r
h
y
th
m
ias with
h
ig
h
p
r
ec
is
io
n
.
−
C
o
n
tin
u
o
u
s
m
o
n
ito
r
in
g
o
f
v
it
al
s
ig
n
s
:
T
h
e
f
r
a
m
ewo
r
k
tr
ac
k
s
k
ey
p
h
y
s
io
lo
g
ical
p
ar
am
et
er
s
s
u
ch
as
h
ea
r
t
r
ate,
en
ab
lin
g
ea
r
ly
id
e
n
tific
atio
n
o
f
p
o
ten
tial c
ar
d
io
v
ascu
lar
an
o
m
alies.
−
Hu
m
an
ac
tiv
ity
r
ec
o
g
n
itio
n
(
HAR)
:
B
y
lev
er
ag
in
g
s
en
s
o
r
d
ata,
th
e
s
y
s
tem
ca
n
ac
c
u
r
ately
class
if
y
p
h
y
s
ical
ac
tiv
ities
s
u
ch
as
walk
in
g
,
jo
g
g
in
g
,
an
d
s
itti
n
g
,
p
r
o
v
id
i
n
g
c
o
n
tex
tu
al
i
n
f
o
r
m
a
tio
n
cr
u
cial
f
o
r
p
atien
t a
s
s
ess
m
en
t.
Desp
ite
th
e
p
o
ten
tial
o
f
I
n
te
r
n
et
o
f
Me
d
ical
T
h
i
n
g
s
(
I
o
MT
)
-
b
ased
r
em
o
te
m
o
n
ito
r
i
n
g
s
y
s
tem
s
,
s
ev
er
al
ch
allen
g
es
m
u
s
t
b
e
ad
d
r
ess
ed
to
en
s
u
r
e
th
eir
ef
f
ec
ti
v
en
ess
an
d
r
eliab
ilit
y
.
Data
p
r
iv
ac
y
an
d
s
ec
u
r
ity
ar
e
p
r
im
a
r
y
co
n
ce
r
n
s
,
as
m
e
d
ical
d
ata
is
h
ig
h
ly
s
en
s
itiv
e
[
6
]
.
E
n
s
u
r
in
g
p
atien
t
p
r
iv
a
cy
wh
ile
e
n
ab
lin
g
c
o
l
l
a
b
o
r
a
t
iv
e
l
e
ar
n
in
g
r
e
m
a
in
s
a
c
r
i
t
i
c
a
l
ch
a
l
l
en
g
e.
F
e
d
er
a
te
d
le
a
r
n
i
n
g
h
e
lp
s
m
i
t
i
g
a
t
e
th
is
i
s
s
u
e
b
y
a
l
lo
w
i
n
g
m
o
d
e
l
u
p
d
a
t
e
s
w
i
th
o
u
t
s
h
a
r
in
g
r
a
w
d
a
t
a
.
Ad
d
i
t
io
n
a
l
ly
,
c
o
m
p
u
ta
t
i
o
n
a
l
co
n
s
tr
a
i
n
t
s
p
o
s
e
a
s
i
g
n
i
f
i
c
an
t
h
u
r
d
l
e,
a
s
w
e
a
r
a
b
le
a
n
d
m
o
b
i
l
e
d
ev
ic
e
s
h
a
v
e
l
i
m
i
t
ed
p
r
o
c
e
s
s
i
n
g
p
o
w
e
r
an
d
b
a
t
t
er
y
l
if
e
.
E
f
f
i
c
ien
t
a
l
g
o
r
i
th
m
s
m
u
s
t
b
e
d
e
s
ig
n
ed
t
o
o
p
e
r
a
t
e
w
i
th
in
t
h
e
s
e
co
n
s
t
r
a
i
n
t
s
.
An
o
th
e
r
m
a
j
o
r
c
h
a
l
le
n
g
e
i
s
t
h
e
h
e
t
e
r
o
g
en
e
o
u
s
n
a
tu
r
e
o
f
d
a
t
a
s
o
u
r
c
e
s
,
a
s
d
i
f
f
e
r
e
n
t
p
ati
e
n
t
s
u
s
e
v
a
r
io
u
s
w
e
ar
a
b
l
e
d
e
v
i
c
e
s
a
n
d
s
en
s
o
r
s
,
l
ea
d
in
g
to
v
a
r
i
at
i
o
n
s
i
n
d
a
t
a
f
o
r
m
a
t
s
,
s
a
m
p
l
in
g
r
a
t
e
s
,
a
n
d
s
i
g
n
a
l
q
u
a
l
i
t
y
.
Mo
r
eo
v
er
,
r
ea
l
-
t
i
m
e
p
r
o
ce
s
s
i
n
g
r
eq
u
ir
e
m
en
t
s
ar
e
c
r
u
c
i
a
l
f
o
r
d
e
t
e
c
t
in
g
p
a
r
o
x
y
s
m
a
l
a
r
r
h
y
t
h
m
i
a
s
,
n
e
ce
s
s
i
t
a
t
i
n
g
in
s
t
a
n
t
an
e
o
u
s
d
a
t
a
a
n
a
l
y
s
i
s
t
o
en
s
u
r
e
t
i
m
e
l
y
in
t
e
r
v
e
n
t
io
n
a
n
d
p
r
ev
en
t
f
a
l
s
e
a
la
r
m
s
[
7
]
–
[
1
1
]
.
L
a
s
t
l
y
,
th
e
l
i
m
i
t
e
d
av
a
i
la
b
i
l
i
t
y
o
f
la
b
e
l
ed
d
a
t
a
m
ak
e
s
i
t
d
i
f
f
ic
u
l
t
to
t
r
a
in
h
i
g
h
l
y
a
c
cu
r
a
t
e
d
e
e
p
l
e
ar
n
in
g
m
o
d
e
l
s
f
r
o
m
s
cr
a
t
c
h
,
a
s
m
e
d
i
c
a
l
d
a
ta
s
e
t
s
a
r
e
o
f
t
en
s
c
ar
c
e
an
d
e
x
p
en
s
i
v
e
t
o
a
n
n
o
t
a
t
e
.
T
h
e
h
ier
ar
ch
ical
f
e
d
er
ated
m
u
lti
-
task
tr
an
s
f
er
lear
n
in
g
f
r
am
ewo
r
k
p
r
esen
ted
in
th
i
s
r
esear
ch
ad
d
r
ess
es th
ese
ch
allen
g
es th
r
o
u
g
h
t
h
e
f
o
llo
win
g
co
n
tr
ib
u
tio
n
s
:
−
A
s
tr
u
ctu
r
ed
FL
a
p
p
r
o
ac
h
is
u
s
ed
wh
er
e
lo
ca
l
m
o
d
els
ar
e
tr
ain
ed
o
n
ed
g
e
d
e
v
ices
an
d
ag
g
r
eg
ated
in
a
clo
u
d
-
b
ased
ce
n
tr
al
s
er
v
er
,
en
s
u
r
in
g
b
o
t
h
p
r
i
v
ac
y
an
d
s
ca
lab
ilit
y
.
−
Kn
o
wled
g
e
g
ain
ed
f
r
o
m
p
r
e
-
tr
ain
ed
m
o
d
els
is
tr
a
n
s
f
er
r
e
d
to
r
elate
d
task
s
,
im
p
r
o
v
in
g
th
e
s
y
s
tem
’
s
ad
ap
tab
ilit
y
to
n
ew
p
atien
ts
an
d
r
ed
u
ci
n
g
th
e
d
ep
en
d
en
cy
o
n
lar
g
e
lab
eled
d
atasets
.
−
T
h
e
s
y
s
tem
em
p
lo
y
s
R
-
p
ea
k
d
etec
tio
n
an
d
QR
in
ter
v
al
d
elin
ea
tio
n
tech
n
iq
u
es
to
en
h
an
ce
t
h
e
ac
cu
r
ac
y
o
f
p
ar
o
x
y
s
m
al
ar
r
h
y
th
m
ia
class
if
icatio
n
.
−
A
u
n
if
ied
m
o
d
el
is
tr
ain
ed
to
r
ec
o
g
n
ize
ac
tiv
ities
ac
r
o
s
s
d
if
f
er
en
t
s
en
s
o
r
d
ev
ices,
en
s
u
r
in
g
r
o
b
u
s
tn
ess
ag
ain
s
t v
ar
iatio
n
s
in
d
ata
c
o
llectio
n
m
eth
o
d
s
.
−
T
h
e
s
y
s
tem
in
clu
d
es
q
u
er
y
-
d
r
iv
en
d
ata
p
r
esen
tatio
n
,
o
v
er
laid
aler
tin
g
m
ec
h
an
is
m
s
,
an
d
ex
p
e
r
t
-
lev
el
v
is
u
aliza
tio
n
to
o
ls
to
en
h
a
n
ce
u
s
ab
ilit
y
wh
ile
m
ain
tain
in
g
p
a
tien
t c
o
n
f
id
en
tiality
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
16
,
No
.
5
,
Octo
b
e
r
20
26
:
2
4
9
4
-
2
5
1
5
2496
2.
RE
L
AT
E
D
WO
RK
S
Fed
er
ated
lear
n
i
n
g
(
FL)
h
as
g
ain
ed
s
ig
n
if
ica
n
t
atten
tio
n
i
n
r
ec
en
t
y
ea
r
s
f
o
r
en
ab
lin
g
d
is
tr
ib
u
ted
m
o
d
el
tr
ain
in
g
w
h
ile
p
r
eser
v
i
n
g
d
ata
p
r
iv
ac
y
.
Ho
wev
e
r
,
e
x
is
tin
g
FL
ap
p
r
o
ac
h
es
f
ac
e
m
u
ltip
le
ch
allen
g
es,
in
clu
d
in
g
m
u
lti
-
task
lear
n
in
g
,
h
eter
o
g
en
e
o
u
s
d
ata,
p
r
iv
ac
y
c
o
n
ce
r
n
s
,
an
d
r
eso
u
r
ce
co
n
s
tr
ai
n
ts
[
1
2
]
.
On
e
m
ajo
r
lim
itatio
n
in
cu
r
r
en
t
FL
r
esear
ch
is
th
e
lac
k
o
f
f
o
cu
s
o
n
m
u
lti
-
task
lear
n
in
g
(
MT
L
)
s
ce
n
ar
io
s
.
T
r
a
d
itio
n
al
ap
p
r
o
ac
h
es
o
f
ten
f
ail
to
d
e
v
el
o
p
a
r
eso
u
r
ce
-
awa
r
e
lea
r
n
in
g
s
tr
ateg
y
s
u
itab
le
f
o
r
r
ea
l
-
wo
r
l
d
ap
p
licatio
n
s
.
T
o
ad
d
r
ess
th
is
,
a
r
eso
u
r
c
e
-
awa
r
e
h
ier
ar
c
h
ical
f
e
d
er
ated
m
u
lti
-
task
lear
n
in
g
(
R
HFed
MT
L
)
[
1
2
]
f
r
am
ewo
r
k
h
as
b
ee
n
p
r
o
p
o
s
ed
.
R
HFed
MT
L
u
tili
ze
s
a
p
r
im
al
-
d
u
al
m
eth
o
d
to
tr
an
s
f
o
r
m
c
o
u
p
le
d
MT
L
p
r
o
b
lem
s
in
to
l
o
ca
l
s
u
b
-
p
r
o
b
lem
s
,
o
p
tim
izin
g
lea
r
n
in
g
ef
f
icien
c
y
.
I
t
im
p
r
o
v
e
s
lear
n
in
g
ac
cu
r
ac
y
an
d
co
n
v
er
g
en
ce
r
ates
b
y
ad
ju
s
tin
g
ter
m
in
al
an
d
b
ase
s
t
atio
n
(
B
S)
iter
atio
n
s
to
en
h
an
ce
r
eso
u
r
ce
ef
f
icien
c
y
.
E
x
p
er
i
m
en
tal
ev
alu
atio
n
s
d
em
o
n
s
tr
ate
th
e
s
u
p
er
io
r
ac
cu
r
ac
y
o
f
R
HFed
MT
L
ac
r
o
s
s
s
e
p
ar
ate
task
s
.
Fed
er
ated
lear
n
in
g
is
v
u
ln
er
ab
le
to
d
ata
p
o
is
o
n
i
n
g
attac
k
s
,
wh
ich
im
p
ac
t
m
o
d
el
g
e
n
er
aliza
b
ilit
y
d
u
e
to
h
eter
o
g
en
e
o
u
s
d
ata
an
d
d
ev
ice
v
ar
iatio
n
s
.
Stu
d
ies
u
s
in
g
th
e
MI
T
-
B
I
H
Ar
r
h
y
th
m
ia
d
atab
ase
(
1
0
9
,
4
4
6
s
am
p
les)
f
o
cu
s
o
n
im
p
r
o
v
i
n
g
elec
tr
o
ca
r
d
io
g
r
am
(
E
C
G)
class
if
icatio
n
v
ia
f
e
d
e
r
ated
tr
an
s
f
er
lear
n
i
n
g
an
d
e
x
p
lain
ab
le
AI
(
XAI
)
.
Dee
p
c
o
n
v
o
lu
ti
o
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs)
tr
ain
ed
o
n
E
C
G
lead
I
I
s
ig
n
als
(
r
esam
p
led
at
1
2
5
Hz)
ac
h
iev
e
d
9
4
.
5
%
ac
cu
r
ac
y
o
n
n
o
is
y
d
ata
an
d
9
8
.
9
%
o
n
clea
n
d
ata.
C
o
m
p
ar
ed
to
p
r
ev
io
u
s
m
eth
o
d
s
,
th
is
f
r
am
ewo
r
k
o
u
tp
e
r
f
o
r
m
s
ex
is
tin
g
wo
r
k
i
n
E
C
G
class
if
ica
tio
n
an
d
p
r
o
v
id
es
im
p
r
o
v
ed
r
o
b
u
s
tn
ess
[
1
3
]
.
On
e
k
e
y
ch
allen
g
e
in
ap
p
ly
i
n
g
d
ee
p
lear
n
i
n
g
t
o
h
ea
lth
ca
r
e
is
th
e
lack
o
f
ex
p
lain
ab
ilit
y
in
m
o
d
els.
Priv
ac
y
co
n
ce
r
n
s
an
d
d
ata
av
ailab
ilit
y
f
u
r
th
er
c
o
m
p
licate
th
e
d
ev
elo
p
m
en
t
o
f
r
o
b
u
s
t
AI
s
o
lu
tio
n
s
.
T
h
e
MI
T
-
B
I
H
Ar
r
h
y
t
h
m
ia
d
ata
b
ase
h
as
b
ee
n
wid
ely
u
s
ed
f
o
r
tr
ain
in
g
an
d
test
in
g
,
b
u
t
n
o
o
th
er
d
atasets
wer
e
co
n
s
id
er
ed
[
1
3
]
.
Usi
n
g
f
ed
er
ated
tr
a
n
s
f
er
lear
n
in
g
a
n
d
ex
p
lain
ab
le
AI
,
r
esear
ch
e
r
s
p
r
o
p
o
s
ed
au
to
e
n
co
d
e
r
a
n
d
C
NN
-
b
ased
class
if
ier
s
f
o
r
ar
r
h
y
th
m
ia
d
etec
tio
n
,
ac
h
iev
in
g
9
4
% a
cc
u
r
ac
y
with
n
o
is
y
d
ata
an
d
9
8
% a
cc
u
r
ac
y
with
clea
n
d
ata.
T
h
e
s
ca
r
city
o
f
tr
ain
in
g
d
ata
p
er
s
en
s
o
r
an
d
th
e
co
m
p
lex
ity
o
f
a
n
aly
zin
g
h
eter
o
g
en
e
o
u
s
m
u
ltiv
ar
iate
tem
p
o
r
al
d
ata
p
r
esen
t
ch
alle
n
g
es
in
ac
tiv
ity
r
ec
o
g
n
itio
n
an
d
en
v
ir
o
n
m
en
t
m
o
n
ito
r
in
g
ap
p
licatio
n
s
.
T
h
e
f
ed
er
ated
m
u
lti
-
task
h
ier
ar
ch
i
ca
l
atten
tio
n
m
o
d
el
(
FATH
O
M)
in
co
r
p
o
r
ates
atten
tio
n
m
e
ch
an
is
m
s
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
s
to
im
p
r
o
v
e
class
if
icatio
n
an
d
r
eg
r
ess
io
n
task
s
[
1
4
]
.
Stu
d
ies
s
h
o
w
th
at
FATH
OM
o
u
tp
er
f
o
r
m
s
co
m
p
etitiv
e
b
aselin
es
an
d
p
r
o
v
es
ef
f
ec
tiv
e
in
ac
tiv
ity
r
ec
o
g
n
itio
n
an
d
en
v
i
r
o
n
m
e
n
tal
m
o
n
ito
r
in
g
task
s
.
A
lar
g
e
-
s
ca
le
E
C
G
d
ataset
f
r
o
m
4
3
,
0
5
9
p
a
tien
ts
co
llected
f
r
o
m
s
ix
g
eo
g
r
ap
h
ically
s
ep
ar
at
e
s
o
u
r
ce
s
h
as
b
ee
n
u
tili
ze
d
in
f
ed
er
ated
lear
n
i
n
g
p
ar
a
d
ig
m
s
.
Var
io
u
s
m
o
d
els,
in
clu
d
in
g
g
r
ad
ien
t
b
o
o
s
tin
g
,
C
NNs,
an
d
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
(
R
NNs)
with
L
STM
s
,
wer
e
e
m
p
lo
y
e
d
,
d
em
o
n
s
tr
atin
g
th
at
FL
ac
h
ie
v
e
d
p
r
ed
ictiv
e
p
e
r
f
o
r
m
an
ce
co
m
p
ar
ab
le
to
ce
n
tr
alize
d
lea
r
n
in
g
.
T
h
e
AI
m
o
d
els
f
o
r
ca
r
d
io
v
a
s
cu
lar
ab
n
o
r
m
ality
d
etec
tio
n
s
h
o
we
d
p
r
o
m
is
in
g
r
esu
lts
,
in
d
icatin
g
t
h
e
f
ea
s
ib
il
ity
o
f
FL
f
o
r
r
ea
l
-
wo
r
ld
h
ea
l
th
ca
r
e
ap
p
licatio
n
s
[
1
5
]
.
T
ab
le
1
s
h
o
ws th
e
co
m
p
ar
is
o
n
o
f
k
ey
f
e
d
er
ated
h
ea
lth
ca
r
e
lear
n
in
g
m
eth
o
d
s
.
T
ab
le
1.
C
o
m
p
a
r
is
o
n
o
f
k
ey
f
e
d
er
ated
h
ea
lth
ca
r
e
lear
n
in
g
m
eth
o
d
s
R
e
f
.
M
e
t
h
o
d
C
o
r
e
I
d
e
a
A
p
p
l
i
c
a
t
i
o
n
Li
mi
t
a
t
i
o
n
[
1
2
]
R
H
F
e
d
M
T
L
H
i
e
r
a
r
c
h
i
c
a
l
f
e
d
e
r
a
t
e
d
m
u
l
t
i
-
t
a
s
k
l
e
a
r
n
i
n
g
w
i
t
h
r
e
s
o
u
r
c
e
o
p
t
i
mi
z
a
t
i
o
n
M
u
l
t
i
-
t
a
sk
I
o
T
sy
s
t
e
ms
N
o
a
t
t
e
n
t
i
o
n
me
c
h
a
n
i
sm
,
l
i
mi
t
e
d
h
e
a
l
t
h
c
a
r
e
f
o
c
u
s
[
1
3
]
F
e
d
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
F
e
d
e
r
a
t
e
d
C
N
N
w
i
t
h
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
a
n
d
e
x
p
l
a
i
n
a
b
l
e
A
I
A
r
r
h
y
t
h
m
i
a
d
e
t
e
c
t
i
o
n
(
M
I
T
-
B
I
H
)
S
i
n
g
l
e
-
t
a
s
k
m
o
d
e
l
[
1
4
]
F
A
TH
O
M
F
e
d
e
r
a
t
e
d
mu
l
t
i
-
t
a
s
k
l
e
a
r
n
i
n
g
w
i
t
h
a
t
t
e
n
t
i
o
n
m
e
c
h
a
n
i
sm
A
c
t
i
v
i
t
y
r
e
c
o
g
n
i
t
i
o
n
N
o
t
r
a
n
sf
e
r
l
e
a
r
n
i
n
g
,
n
o
t
h
e
a
l
t
h
c
a
r
e
-
s
p
e
c
i
f
i
c
[
1
6
]
M
TG
C
-
H
F
L
G
r
a
d
i
e
n
t
c
o
r
r
e
c
t
i
o
n
f
o
r
h
i
e
r
a
r
c
h
i
c
a
l
f
e
d
e
r
a
t
e
d
l
e
a
r
n
i
n
g
D
i
st
r
i
b
u
t
e
d
c
l
a
ssi
f
i
c
a
t
i
o
n
N
o
m
u
l
t
i
-
t
a
sk
s
u
p
p
o
r
t
[
1
7
]
Emb
e
d
d
e
d
E
C
G
AI
Lo
w
-
p
o
w
e
r
A
I
-
b
a
se
d
a
r
r
h
y
t
h
mi
a
d
e
t
e
c
t
i
o
n
R
e
a
l
-
t
i
me
EC
G
mo
n
i
t
o
r
i
n
g
N
o
f
e
d
e
r
a
t
e
d
l
e
a
r
n
i
n
g
,
n
o
p
r
i
v
a
c
y
f
r
a
mew
o
r
k
[
1
8
]
C
N
N
EC
G
m
o
d
e
l
D
e
e
p
l
e
a
r
n
i
n
g
u
s
i
n
g
R
R
i
n
t
e
r
v
a
l
f
e
a
t
u
r
e
s
A
r
r
h
y
t
h
m
i
a
c
l
a
ssi
f
i
c
a
t
i
o
n
C
e
n
t
r
a
l
i
z
e
d
m
o
d
e
l
,
l
a
c
k
s
p
r
i
v
a
c
y
Pr
o
p
o
sed
HF
-
M
T
T
L
Hi
e
r
a
r
c
h
i
c
a
l
F
L
+
M
T
L
+
T
L
+
A
t
t
e
n
t
i
o
n
E
C
G + HA
R
+
V
i
t
a
l
M
o
n
i
t
o
r
i
n
g
Hi
g
h
e
r
c
o
mpu
t
a
t
i
o
n
a
l
c
o
mpl
e
x
i
t
y
E
x
is
tin
g
h
ier
ar
c
h
ical
f
ed
e
r
ate
d
lear
n
in
g
(
HFL)
alg
o
r
it
h
m
s
s
tr
u
g
g
le
with
m
u
lti
-
tim
escale
m
o
d
el
d
r
if
t,
lead
in
g
to
wo
r
s
en
ed
co
n
v
er
g
en
ce
b
o
u
n
d
s
with
in
cr
ea
s
in
g
d
ata
h
eter
o
g
en
eity
.
T
h
e
im
p
ac
t
o
f
lab
el
s
h
if
t
(
3
class
es
p
er
g
r
o
u
p
,
2
class
es
p
er
clien
t)
an
d
f
ea
tu
r
e
s
h
if
t
(
i
m
ag
e
r
o
tatio
n
d
if
f
er
e
n
ce
s
ac
r
o
s
s
clien
ts
)
f
u
r
th
e
r
co
m
p
licates
FL
p
er
f
o
r
m
an
ce
.
T
o
ad
d
r
ess
th
ese
ch
allen
g
es,
a
m
u
lti
-
tim
escale
g
r
ad
ien
t
co
r
r
ec
tio
n
(
MT
GC
)
m
eth
o
d
o
l
o
g
y
was
p
r
o
p
o
s
ed
,
in
tr
o
d
u
cin
g
d
is
tin
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l
v
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iab
les
f
o
r
g
r
ad
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o
r
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ctio
n
[
1
6
]
.
MT
GC
s
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if
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tp
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r
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m
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ith
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[
1
9
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.
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2497
Desp
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[
1
7
]
,
[
1
8
]
.
2
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1
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Resea
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lear
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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,
Vo
l.
16
,
No
.
5
,
Octo
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20
26
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3.
P
RO
P
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M
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T
H
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Y
3
.
1
.
Da
t
a
s
et
d
escript
io
n
T
o
ev
alu
ate
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p
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o
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m
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ed
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ier
ar
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h
ica
l
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ated
m
u
lti
-
task
tr
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s
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n
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r
am
ewo
r
k
,
we
u
tili
ze
d
two
p
u
b
licly
av
ailab
le
d
atasets
:
th
e
MI
T
-
B
I
H
Ar
r
h
y
th
m
ia
d
atab
a
s
e
f
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ar
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al
ar
r
h
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m
ia
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etec
tio
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d
th
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HAR
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atase
t
f
o
r
ac
tiv
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g
n
itio
n
.
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h
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d
atasets
p
r
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v
id
e
h
ig
h
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q
u
ality
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ea
l
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wo
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ld
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n
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tiv
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ased
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ig
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als
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ess
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r
m
u
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p
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3
.
1
.
1
.
MIT
-
B
I
H
Arr
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t
hm
ia
d
a
t
a
ba
s
e
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e
MI
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Ar
r
h
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m
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ase
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s
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en
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ar
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ataset
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elec
tr
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r
d
io
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an
aly
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is
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d
ar
r
h
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m
ia
d
etec
tio
n
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t
co
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ts
o
f
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r
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s
f
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led
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6
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V.
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tes
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v
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iety
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ts
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m
al
ar
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t
h
m
ias.
T
h
e
f
iv
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class
es
ar
e
n
o
r
m
al,
atr
ial
p
r
em
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r
e,
v
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tr
icu
lar
p
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em
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r
e
,
lef
t b
u
n
d
le
b
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an
c
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b
lo
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k
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r
ig
h
t b
u
n
d
le
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an
ch
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lo
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.
3
.
1
.
2
.
H
um
a
n
a
ct
iv
it
y
re
co
g
n
it
io
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(
H
AR)
d
a
t
a
s
et
T
h
e
HAR
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ataset
,
co
llected
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r
o
m
wea
r
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le
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en
s
o
r
s
o
n
3
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d
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id
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als
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is
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s
ed
to
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ain
t
h
e
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ity
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ec
o
g
n
itio
n
m
o
d
u
le.
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h
e
d
ata
s
et
co
n
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is
ts
o
f
ac
ce
ler
o
m
eter
an
d
g
y
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o
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co
p
e
d
ata
r
ec
o
r
d
e
d
f
r
o
m
a
Sam
s
u
n
g
Gala
x
y
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I
I
s
m
ar
tp
h
o
n
e
p
lace
d
o
n
th
e
s
u
b
jects'
waist
d
u
r
in
g
d
if
f
er
e
n
t
ac
tiv
ities
.
T
h
e
6
class
es
ar
e
walk
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g
,
walk
in
g
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p
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tair
s
,
walk
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d
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wn
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i
ttin
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h
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Fig
u
r
e
1
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ab
le
3
s
h
o
ws
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e
d
ataset
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u
m
m
ar
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o
f
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-
B
I
H
Ar
r
h
y
th
m
ia
d
ataset
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d
HAR
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ataset.
Fig
u
r
e
1
.
Dis
tr
ib
u
tio
n
o
f
ac
tiv
i
ties
in
HAR
d
ataset
T
ab
le
3
.
Su
m
m
a
r
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o
f
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r
h
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m
ia
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ataset
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d
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47
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I
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2499
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h
ier
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ch
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er
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ito
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al
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h
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ias,
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o
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ig
n
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ical
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3
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2
.
P
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pro
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T
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ab
le
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if
icatio
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th
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ier
ar
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ate
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m
u
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r
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ased
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ec
o
m
p
o
s
ed
u
s
in
g
wav
el
et
tr
an
s
f
o
r
m
s
,
a
n
d
co
ef
f
icien
ts
co
r
r
esp
o
n
d
in
g
to
n
o
is
e
ar
e
th
r
esh
o
l
d
ed
.
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h
e
d
en
o
is
ed
s
ig
n
al
′
(
)
is
r
ec
o
n
s
tr
u
cted
u
s
in
g
:
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(
)
=
∑
=
1
.
(
)
(
1
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wh
er
e
r
ep
r
esen
ts
th
e
wav
elet
co
ef
f
icien
ts
,
an
d
(
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is
th
e
s
el
ec
ted
m
o
th
er
wav
elet
f
u
n
ctio
n
.
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r
E
C
G
s
ig
n
als,
ad
d
itio
n
al
f
ilter
in
g
is
p
er
f
o
r
m
ed
u
s
in
g
a
b
an
d
-
p
ass
f
ilter
,
wh
ich
elim
in
ates
f
r
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en
cies
o
u
ts
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e
th
e
0
.
5
–
5
0
Hz
r
a
n
g
e,
e
x
p
r
ess
ed
as:
(
)
=
(
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−
[
∑
(
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∄
(
0
.
5
,
50
)
]
(
2
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wh
er
e
(
)
is
th
e
o
r
ig
in
al
E
C
G
s
ig
n
al,
an
d
t
h
e
s
u
m
m
atio
n
ter
m
r
em
o
v
es
f
r
e
q
u
en
c
y
co
m
p
o
n
e
n
ts
o
u
ts
id
e
th
e
d
esire
d
r
an
g
e
.
Fo
llo
win
g
d
en
o
is
in
g
,
n
o
r
m
al
izatio
n
is
ap
p
lied
to
s
tan
d
a
r
d
ize
s
ig
n
al
v
alu
es,
en
s
u
r
in
g
co
n
s
is
ten
t
f
ea
tu
r
e
d
is
tr
ib
u
tio
n
s
ac
r
o
s
s
d
if
f
er
en
t su
b
jects.
Min
-
Ma
x
n
o
r
m
aliza
tio
n
is
u
s
ed
to
s
ca
le
v
alu
es b
etwe
en
0
an
d
1
u
s
in
g
(
3
)
:
=
−
−
(
3
)
wh
er
e
r
ep
r
esen
ts
th
e
r
aw
d
ata,
an
d
,
ar
e
th
e
m
in
im
u
m
an
d
m
ax
i
m
u
m
v
alu
es,
r
esp
ec
tiv
ely
.
Ad
d
itio
n
ally
,
f
o
r
ze
r
o
-
ce
n
ter
e
d
d
ata
d
is
tr
ib
u
tio
n
,
Z
-
s
co
r
e
n
o
r
m
aliza
tio
n
is
em
p
lo
y
e
d
:
′
=
−
(
4
)
wh
er
e
an
d
d
en
o
te
th
e
m
ea
n
a
n
d
s
tan
d
ar
d
d
ev
iatio
n
o
f
t
h
e
d
ataset.
Af
ter
n
o
r
m
aliza
tio
n
,
th
e
s
eg
m
en
tatio
n
p
r
o
ce
s
s
ex
tr
ac
ts
r
ele
v
an
t
s
ig
n
al
win
d
o
ws
f
o
r
an
al
y
s
is
.
Giv
en
an
in
p
u
t
s
ig
n
al
(
)
,
a
s
lid
in
g
win
d
o
w
o
f
s
ize
is
ap
p
lied
with
a
n
o
v
e
r
lap
f
ac
to
r
,
p
r
o
d
u
ci
n
g
s
eg
m
en
ts
d
ef
in
ed
as
(
5
)
:
S
=
(
:
+
)
,
=
(
)
(
5
)
wh
er
e
d
en
o
tes th
e
s
eg
m
en
t in
d
ex
.
T
h
is
s
tep
en
s
u
r
es th
e
m
o
d
el
ca
p
tu
r
es tem
p
o
r
al
v
ar
iatio
n
s
ef
f
ec
tiv
ely
.
Feat
u
r
e
ex
tr
ac
t
io
n
is
t
h
e
n
p
er
f
o
r
m
e
d
t
o
d
er
i
v
e
m
ea
n
i
n
g
f
u
l
p
a
tte
r
n
s
.
Fo
r
E
C
G
s
i
g
n
al
s
,
R
-
p
e
a
k
s
an
d
QR
i
n
t
e
r
v
als
a
r
e
i
d
e
n
ti
f
i
ed
u
s
in
g
Pa
n
-
T
o
m
p
k
i
n
s
a
n
d
w
av
elet
t
r
a
n
s
f
o
r
m
s
,
w
h
e
r
e
R
-
p
ea
k
d
et
ec
ti
o
n
f
o
ll
o
ws:
=
min
∈
[
,
+
1
]
(
)
(
6
)
en
s
u
r
in
g
p
r
ec
is
e
ar
r
h
y
th
m
ia
cl
ass
if
icatio
n
.
Fo
r
ac
tiv
ity
r
ec
o
g
n
itio
n
,
s
tatis
tical
an
d
f
r
eq
u
en
c
y
-
d
o
m
ai
n
f
ea
tu
r
es
s
u
ch
as
m
ea
n
ab
s
o
lu
te
d
ev
iatio
n
(
MA
D)
an
d
s
p
ec
tr
al
en
tr
o
p
y
ar
e
co
m
p
u
ted
as:
=
1
∑
∣
−
̅
∣
=
1
(
7
)
(
)
=
−
∑
(
)
(
)
(
8
)
wh
er
e
(
)
r
ep
r
esen
ts
th
e
p
r
o
b
ab
il
ity
d
is
tr
ib
u
tio
n
o
f
s
p
ec
tr
al
co
m
p
o
n
e
n
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
16
,
No
.
5
,
Octo
b
e
r
20
26
:
2
4
9
4
-
2
5
1
5
2500
Fin
ally
,
d
ata
au
g
m
en
tatio
n
ad
d
r
ess
es
cla
s
s
im
b
alan
ce
s
b
y
g
en
er
atin
g
s
y
n
th
etic
d
ata
u
s
in
g
Gau
s
s
ian
n
o
is
e
in
jectio
n
,
ex
p
r
ess
ed
as
(
9
)
:
=
+
,
∼
(
0
,
2
)
(
9
)
wh
er
e
ϵ
is
Gau
s
s
ian
n
o
is
e
with
v
ar
ian
ce
2
.
B
y
in
teg
r
atin
g
th
ese
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
,
th
e
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
e
n
s
u
r
es
clea
n
,
s
tan
d
a
r
d
ized
,
a
n
d
r
ep
r
esen
tativ
e
d
ata,
im
p
r
o
v
in
g
th
e
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
es
s
o
f
ar
r
h
y
th
m
ia
d
etec
tio
n
,
v
ital
s
ig
n
m
o
n
ito
r
in
g
,
an
d
ac
tiv
ity
r
ec
o
g
n
itio
n
.
3
.
3
.
P
r
o
po
s
ed
m
et
ho
do
lo
g
y
T
h
e
p
r
o
p
o
s
ed
h
ier
a
r
ch
ical
f
e
d
er
ated
m
u
lti
-
task
tr
an
s
f
er
le
ar
n
in
g
f
r
am
ewo
r
k
is
d
esig
n
e
d
to
ad
d
r
ess
th
e
ch
allen
g
es
o
f
p
r
iv
ac
y
-
p
r
e
s
er
v
in
g
,
r
ea
l
-
tim
e,
an
d
m
u
lti
-
task
p
h
y
s
io
lo
g
ical
m
o
n
ito
r
in
g
.
T
r
ad
itio
n
al
clo
u
d
-
b
ased
m
o
d
els
f
o
r
h
ea
lth
ca
r
e
ap
p
licatio
n
s
r
aise
co
n
ce
r
n
s
r
e
g
ar
d
in
g
d
ata
p
r
iv
ac
y
,
laten
cy
,
an
d
s
ca
lab
ilit
y
,
as
p
atien
t
d
ata
m
u
s
t
b
e
tr
an
s
m
itted
an
d
s
to
r
ed
in
ce
n
tr
alize
d
lo
ca
tio
n
s
.
T
o
o
v
e
r
co
m
e
th
ese
lim
itatio
n
s
,
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
lev
er
ag
e
s
FL,
MT
T
L
,
an
d
d
ee
p
lear
n
i
n
g
to
en
ab
le
on
-
d
ev
ice
m
o
d
el
tr
ain
in
g
,
cr
o
s
s
-
task
k
n
o
wled
g
e
t
r
an
s
f
er
,
a
n
d
clo
u
d
-
b
ased
f
in
e
-
t
u
n
in
g
.
T
h
e
p
r
im
ar
y
o
b
jectiv
es o
f
th
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
ar
e:
−
Dec
en
tr
alize
d
an
d
s
ec
u
r
e
m
o
d
el
tr
ain
in
g
–
u
s
in
g
FL,
m
o
d
el
u
p
d
ates
a
r
e
ag
g
r
eg
ate
d
wit
h
o
u
t
s
h
a
r
in
g
r
aw
p
atien
t d
ata,
en
s
u
r
i
n
g
p
r
iv
ac
y
an
d
s
ec
u
r
ity
.
−
Mu
lti
-
task
lear
n
in
g
f
o
r
ef
f
icie
n
t
f
ea
tu
r
e
s
h
ar
i
n
g
–
A
s
h
ar
ed
d
ee
p
lear
n
in
g
r
e
p
r
esen
tatio
n
is
em
p
lo
y
e
d
f
o
r
m
u
ltip
le
h
ea
lth
ca
r
e
task
s
,
in
clu
d
in
g
ar
r
h
y
th
m
ia
d
etec
tio
n
,
h
u
m
an
ac
tiv
ity
r
ec
o
g
n
itio
n
(
HAR)
,
an
d
v
ital
s
ig
n
m
o
n
ito
r
i
n
g
.
−
W
av
elet
-
b
ased
s
ig
n
al
p
r
ep
r
o
ce
s
s
in
g
f
o
r
n
o
is
e
r
ed
u
ctio
n
–
Ph
y
s
io
lo
g
ical
s
ig
n
als
ar
e
d
en
o
is
ed
u
s
in
g
a
wav
elet
tr
an
s
f
o
r
m
atio
n
,
p
r
eser
v
in
g
cr
itical
in
f
o
r
m
atio
n
f
o
r
m
o
d
el
tr
ain
in
g
.
−
Hier
ar
ch
ical
lear
n
in
g
ar
ch
itect
u
r
e
–
T
h
e
f
r
a
m
ewo
r
k
is
d
esig
n
ed
in
a
t
h
r
ee
-
tier
ed
s
tr
u
ctu
r
e
co
m
p
r
is
in
g
th
e
ed
g
e
l
ay
er
(
wea
r
ab
le
d
e
v
ices)
,
f
ed
er
ated
lea
r
n
in
g
la
y
er
(
FL
s
er
v
er
)
,
an
d
clo
u
d
l
ay
er
(
g
lo
b
al
o
p
tim
izatio
n
with
MT
T
L
)
,
en
s
u
r
in
g
s
ca
lab
i
lity
an
d
ef
f
icien
c
y
.
T
h
is
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
en
s
u
r
es
th
at
r
ea
l
-
tim
e
p
atien
t
m
o
n
ito
r
in
g
c
an
b
e
ac
h
iev
e
d
wit
h
o
u
t
c
o
m
p
r
o
m
is
in
g
d
ata
p
r
iv
ac
y
wh
ile
im
p
r
o
v
in
g
m
o
d
el
g
en
er
aliza
tio
n
ac
r
o
s
s
h
ea
lth
ca
r
e
task
s
.
T
h
e
p
r
o
p
o
s
ed
f
r
am
ewo
r
k
co
n
s
is
ts
o
f
th
r
ee
k
ey
lay
e
r
s
:
ed
g
e
lay
er
,
f
ed
er
ated
lear
n
in
g
lay
er
,
a
n
d
clo
u
d
lay
er
.
3
.
3
.
1
.
E
dg
e
l
a
y
er
:
Wea
ra
ble
s
ens
o
r
da
t
a
a
cquis
it
io
n a
nd
prepro
ce
s
s
i
ng
Fig
u
r
e
2
s
h
o
ws
d
etec
tio
n
o
f
A
r
r
h
y
th
m
ia
u
s
in
g
E
C
G
s
ig
n
als
with
FL
.
I
n
t
h
e
ed
g
e
lay
e
r
,
wea
r
ab
le
d
ev
ices
an
d
s
en
s
o
r
s
ac
q
u
ir
e
r
aw
p
h
y
s
io
lo
g
ical
s
ig
n
als,
s
u
c
h
as
E
C
G
wav
ef
o
r
m
s
(
)
ac
ce
ler
o
m
eter
s
ig
n
als
(
)
,
an
d
g
y
r
o
s
co
p
e
r
ea
d
i
n
g
s
(
)
,
wh
er
e:
(
)
=
(
)
+
(
)
(
1
0
)
(
)
=
(
)
+
(
)
(
1
1
)
(
)
=
(
)
+
(
)
(
1
2
)
Fig
u
r
e
2
.
Dete
ctio
n
o
f
ar
r
h
y
t
h
m
ia
u
s
in
g
E
C
G
s
ig
n
als with
f
ed
er
ated
lear
n
i
n
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
h
iera
r
ch
ica
l fe
d
era
ted
mu
lti
-
ta
s
k
tr
a
n
s
fer lea
r
n
in
g
fr
a
mewo
r
k
fo
r
…
(
P
o
o
ma
r
i D
u
r
g
a
K
.
)
2501
Her
e,
r
ep
r
esen
ts
th
e
ac
tu
al
p
h
y
s
io
lo
g
ical
s
ig
n
al,
an
d
d
e
n
o
tes
n
o
is
e
in
ter
f
er
en
ce
,
wh
i
ch
is
m
in
im
ized
u
s
in
g
wav
elet
-
b
ased
d
e
n
o
is
in
g
:
′
(
)
=
∑
=
1
⋅
(
)
(
1
4
)
wh
er
e
ar
e
wav
elet
co
ef
f
icien
t
s
an
d
(
)
is
th
e
m
o
th
er
wav
elet
f
u
n
ctio
n
.
Alg
o
r
ith
m
1
.
T
r
ain
in
g
o
f
ed
g
e
d
ev
ice
Input: Raw physiological signals (ECG, Accelerometer, Gyroscope)
Processing: Wavelet
-
based denoising and local model training
Output: Locally trained model parameters sent to the FL server
Function EdgeDevice_Training():
Collect raw sensor data: ECG_signal, ACC_signal, GYR_signal
Apply wavelet transform for noise reduction
Extract denoised signals: S'_ECG, S'_ACC, S'_GYR
Preprocess data for training
Initialize local model M
i
For each epoch:
Compute local gradients using SGD
Update local model parameters θ
i
Send θ
i
to Federated Learning Server
End Function
a.
Par
o
x
y
s
m
al
ar
r
h
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th
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ia
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etec
tio
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al
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o
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le
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o
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r
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o
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ed
m
o
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le
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r
ates
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ialized
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ig
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n
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ee
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o
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ig
h
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iag
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ir
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ig
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itical
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ciate
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r
d
iac
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en
ts
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R
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ea
k
d
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o
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ith
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tial
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tify
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ate
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ig
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r
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ial
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ib
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Fig
u
r
e
3
s
h
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ig
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ased
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ip
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atter
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o
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r
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h
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ate
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o
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ef
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im
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r
o
v
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g
class
if
icatio
n
ac
cu
r
ac
y
an
d
in
ter
p
r
etab
ilit
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
16
,
No
.
5
,
Octo
b
e
r
20
26
:
2
4
9
4
-
2
5
1
5
2502
Fig
u
r
e
3
.
E
C
G
s
ig
n
al
with
p
ea
k
d
etec
tio
n
T
h
e
f
in
al
class
if
icatio
n
m
o
d
el
ass
ig
n
s
a
p
r
o
b
ab
ilit
y
s
co
r
e
to
ea
ch
r
h
y
t
h
m
ca
teg
o
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y
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s
in
g
a
So
f
tMa
x
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u
n
ctio
n
:
(
)
=
∑
=
1
(
1
8
)
wh
er
e
r
ep
r
esen
ts
th
e
lo
g
its
o
f
class
,
an
d
is
th
e
n
u
m
b
er
o
f
r
h
y
th
m
ca
teg
o
r
ies
(
e.
g
.
,
N
o
r
m
a
l,
AFib
,
PVC
,
Ven
tr
icu
lar
T
ac
h
y
ca
r
d
ia)
.
T
h
e
m
o
d
u
le
is
im
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lem
e
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ted
at
th
e
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g
e
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er
o
f
t
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e
h
ier
ar
ch
ical
f
ed
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ated
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r
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lin
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-
tim
e
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e
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en
ce
o
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r
ab
le
d
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ices.
T
h
e
lo
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l
m
o
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els,
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ain
e
d
o
n
-
d
e
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ice,
ar
e
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e
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d
icall
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p
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ated
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f
ed
e
r
ated
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e
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g
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Av
g
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ef
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t
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atien
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ata
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alize
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er
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er
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Fig
u
r
e
4
s
h
o
ws
th
e
ar
ch
itectu
r
e
o
f
t
h
e
f
ed
e
r
ated
lear
n
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g
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t
h
e
f
ed
er
ate
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lear
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e
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r
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r
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e
d
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r
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ata
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r
o
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d
.
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h
e
lo
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l m
o
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el
o
n
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e
v
ice
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p
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ated
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s
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g
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to
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g
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ad
ie
n
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escen
t
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:
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=
−
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,
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(
1
9
)
wh
er
e
r
ep
r
esen
ts
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o
d
el
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ar
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eter
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at
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e
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η
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e
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ar
n
in
g
r
ate,
an
d
is
th
e
lo
ca
l
lo
s
s
f
u
n
ctio
n
co
m
p
u
ted
o
n
d
ata
(
,
)
.
T
h
e
lo
ca
l
u
p
d
ates a
r
e
ag
g
r
eg
ated
at
th
e
ce
n
tr
al
FL
s
er
v
er
u
s
in
g
f
ed
e
r
a
ted
av
er
ag
in
g
(
Fed
Av
g
)
:
+
1
=
∑
=
1
(
2
0
)
wh
er
e
is
th
e
to
tal
n
u
m
b
er
o
f
p
ar
ticip
atin
g
d
ev
ices a
n
d
is
th
e
d
ata
co
n
tr
ib
u
tio
n
f
r
o
m
d
ev
ice
.
Alg
o
r
ith
m
2
.
Fed
er
ated
lear
n
in
g
Input: Local model updates from multiple edge devices
Processing: Federated Averaging (FedAvg) to aggregate models
Output: Updated global model sent to edge devices
Function Federated_Learning():
Initialize global model
+
1
Receive local model updates
θ
i
from N edge devices
Aggregate models using Federated Averaging:
+
1
=
∑
=
1
Send updated global model
+
1
back to edge devices
End Function
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
h
iera
r
ch
ica
l fe
d
era
ted
mu
lti
-
ta
s
k
tr
a
n
s
fer lea
r
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g
fr
a
mewo
r
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r
…
(
P
o
o
ma
r
i D
u
r
g
a
K
.
)
2503
Fig
u
r
e
4
.
Ar
c
h
itectu
r
e
o
f
th
e
f
ed
er
ated
lear
n
i
n
g
T
h
e
clo
u
d
lay
er
o
p
tim
izes
m
o
d
el
p
er
f
o
r
m
an
ce
u
s
in
g
MTTL
,
wh
ich
f
in
e
-
tu
n
es
s
h
ar
ed
r
ep
r
e
s
en
tatio
n
s
ac
r
o
s
s
task
s
.
T
h
e
tr
an
s
f
er
lear
n
in
g
f
u
n
ctio
n
is
m
o
d
eled
as:
=
1
ℎ
ℎ
+
2
+
3
(
2
1
)
wh
er
e
ar
e
weig
h
tin
g
f
ac
to
r
s
f
o
r
d
if
f
er
e
n
t
task
s
.
T
h
e
o
b
je
ctiv
e
f
u
n
ctio
n
f
o
r
ar
r
h
y
t
h
m
ia
class
if
icatio
n
is
f
o
r
m
u
lated
as a
cr
o
s
s
-
en
tr
o
p
y
lo
s
s
:
ℎ
ℎ
=
−
∑
l
og
̂
=
1
(
2
2
)
wh
er
e
is
th
e
n
u
m
b
er
o
f
ar
r
h
y
t
h
m
ia
class
es,
is
th
e
tr
u
e
lab
el,
an
d
̂
is
th
e
p
r
ed
icted
p
r
o
b
a
b
ilit
y
.
Similar
ly
,
th
e
task
is
o
p
tim
ize
d
u
s
in
g
ca
teg
o
r
ical
cr
o
s
s
-
en
tr
o
p
y
:
=
−
∑
l
og
̂
=
1
(
2
3
)
wh
er
e
is
th
e
n
u
m
b
er
o
f
ac
tiv
it
y
class
es.
Alg
o
r
ith
m
3
.
C
lo
u
d
o
p
tim
izatio
n
Input: Global model from FL layer
Processing: Fine
-
tuning for different healthcare tasks
Output: Optimized model for deployment
Function Cloud_Optimization():
Receive global model
+
1
Fine
-
tune for multiple tasks using MTTL:
Compute loss functions:
ℎ
ℎ
=
(
)
=
(
)
=
(
)
Compute weighted multi
-
task loss:
=
1
ℎ
ℎ
+
2
+
3
Optimize model parameters using
Deploy final optimized model for inference
End Function
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