Deep Federated Learning for IoT-based
Decentralized Healthcare Systems
Haya Elayan1 , Moayad Aloqaily2 , Mohsen Guizani2
1
2021 International Wireless Communications and Mobile Computing (IWCMC) | 978-1-7281-8616-0/21/$31.00 ©2021 IEEE | DOI: 10.1109/IWCMC51323.2021.9498820
xAnalytics Inc., Ottawa, ON, Canada.
2
Qatar University, Doha, Qatar.
E-mails: h.elayan@xanalytics.ca, maloqaily@ieee.org, mguizani@ieee.org
Abstract—Recent trends in the healthcare industry, such as
the use of wearable IoT for continuous health monitoring, are
setting new requirements for healthcare systems that boost data
analysis. These systems should support decentralization and
maintain the privacy and ownership of users’ data due to the
sensitivity of healthcare data. Therefore, the use of federated
learning techniques is recommended for systems that need such
requirements. This paper proposes a Deep Federated Learning
framework for decentralized healthcare systems that maintain
user privacy in a distributed architecture. It also proposes an
algorithm for an automated training data acquiring process.
Furthermore, it presents an experiment for using deep federated
learning in detecting skin diseases and using Transfer Learning
to address the problem of limited availability of healthcare data
in building deep learning models. The evaluated results show
how the federated learning increased the Area Under the Curve
of the centralized learning model up to 0.97, as it also shows
good model performance during federated rounds in terms of
accuracy, precision, recall, and F1-score. Moreover, although the
FL system has affected the quality of service to the user in terms
of model conversion time, the Federated Learning system meets
the requirements of building models in a decentralized manner
with no sharing of users’ private data.
Index Terms—Deep Federated Learning, Healthcare, IoT,
Transfer Learning, Distributed systems, Privacy.
I. I NTRODUCTION
Over the past few years, traditional technologies have become insufficient to create privacy-preserving applications as
more and more records are exposed, especially with the spread
of internet-connected devices as the global average of mobile
internet data traffic is expected to reach 48,72 PB per month
in 2021 [1]. Specifically for industrial healthcare applications
where the healthcare industry is the most expensive in the average cost of data breaches worldwide [2]. Therefore, it is highly
recommended to use technologies that support decentralization
and maintain data ownership to achieve more privacy.
Federated learning (FL) is a machine learning technique
used to train machine learning models collaboratively in a
decentralized manner on multiple devices or servers, thus
maintain data privacy and maintain data ownership for the
device/server owner [3]. FL is hugely beneficial for highly
decentralized healthcare data, especially after the increasing
popularity of the use of IoT devices to capture data and
monitor health continuously, as the healthcare IoT market is
expected to reach $ 158 billion in 2022 according to Deloitte
estimates [4]. It is also beneficial for the sensitivity of this data
because the health information of the users is very sensitive
to disclose [5]. FL will train machine learning models locally
without sharing data, unlike centralized learning techniques
978-1-7281-8616-0/21/$31.00 ©2021 IEEE
where the training process happens in a centralized unit.
Alternatively, local models will share their updates after each
local training process and those updates will be aggregated
to train a global model. Since there is an on-device training
process in FL which means less human interaction with the
local model or the captured data, it becomes essential to use
advanced machine learning algorithms such as Deep Learning
to tackle this process.
Deep Federated Learning (DFL) uses deep neural networks
with FL to train models as deep learning can build robust
models and reduce feature engineering processes. This is
helpful for healthcare data where it is huge and ever-changing
which requires feature engineering techniques to extract the
most useful features for building models. Healthcare data
also faces the problem of being unavailable or having limited
samples [6] which makes it require other machine learning
techniques to build high-performance models such as transfer
learning. Transfer Learning is a method that uses knowledge
gained by solving a problem while training a model in solving
a related problem for another model. Deep learning has shown
its effectiveness in various healthcare applications, especially
in computer vision for medical imaging, such as detection of
diabetic retinopathy in fundus images [7] and classification of
skin cancer images [8].
DFL’s integration with IoT will help build robust privacypreserving applications in a decentralized manner as well as
meet a series of challenges. As IoT devices still face some AI
reliability and compatibility issues. However, the digitization
and AI-market growth will boost the IoT industry [9].
This article integrates these technologies and applies various
techniques for analyzing healthcare data that aid in patients’
health monitoring. The contributions of this article can be
summarized as follows:
•
•
•
•
Propose a DFL framework for healthcare data analysis
using IoT,
Introduce an algorithm for DFL process automation that
handles the training data acquiring stage,
Use Transfer Learning for building a skin-diseases detection model,
Implement an experiment for skin-diseases detection using Deep Federated Learning.
The reminder of this paper is organized as follows. Section II
discusses the related work. Section III describes the proposed
DFL for decentralized healthcare systems framework. Section
IV proposes the data acquiring algorithm. Section V shows
system implementation and setup. Section VI discusses the
105
evaluation metrics and results. Lastly, Section VII presents the
conclusion of this paper.
TABLE I
R ELATED W ORK S UMMARY
II. R ELATED W ORK
Use FL
Use IoT
[10]
[11]
[12]
[14]
[15]
[16]
[17]
[19]
[20]
[21]
[22]
[24]
[25]
[26]
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
No
No
No
No
Yes
No
Federated Learning
(Global model)
Cloud Computing
AI-Ready Server
Global Training
Local models'
updates
Global model
Global model
Global model
Global model
Federated Learning
(Local models)
Internet of Things
Local
Training
III. A F RAMEWORK : DFL FOR I OT- BASED
D ECENTRALIZED H EALTHCARE S YSTEMS
Implemented Healthcare
Application
HAR
HAR
HAR
ECG Classification
X-ray Classification
X-ray Classification
EHR
EHR
EHR
-
will capture users’ data and train a local deep learning model
which is a copy of a previously received global model. After
completing the local training process, the models will work
collaboratively on training a global model using their updates
rather than using the users’ raw data. These models’ updates
are the changes to the models’ weights during the training
process and they do not reflect any private or personal data of
the users.
Global model
In integrating FL with IoT in healthcare, authors of [10]
[11] [12] introduced FL for Human Activity Recognition for
remote healthcare monitoring by proposing FL systems using
IoT devices. Authors in [13] integrated FL with digital twin
for IoT to preserve users’ privacy. Sun et. al. [14] proposed a
framework for improving IoT digital twins learning efficiency
using FL. In healthcare monitoring context, the authors of [15]
presented a digital twin framework using IoT for healthcare
monitoring without adding FL to the process. They tested
their work on several algorithms using ECG data where the
results showed that deep learning performed the best across
all implemented algorithms.
Others such as [16] and [17] proposed dedicated FL frameworks for detecting COVID-19 infection using X-ray images.
The latter has applied transfer learning to several pre-trained
models, based on the results, ResNet18 showed the best performance. Rahman et. al. [18] proposed FL framework for healthIoT by adding an edge layer for performing deep learning
tasks and blockchain for more security and trustworthiness.
Likewise, authors of [19] introduced a mechanism for sharing
industrial IoT data using FL and blockchain. They evaluated
their proposed work on text categorization dataset.
Also in the healthcare domain, authors in [20]–[23] proposed work in FL for EHRs and all of them have shown
promising results in this area. The first performed a clustering
technique to create community-based data that had clinical
meaning. The results showed that the clustering-based FL
model exceeds the performance of the normal FL model.
Another contribution in other industries, Yu et. al. [24]
proposed an abnormal weights-clipping FL algorithm based on
Federated Average technique to improve its performance and
applied it to a general objects detection dataset. In another
study [25], authors proposed an FL framework and applied
it to vehicle image classification. Finally, the author of [26]
proposed an FL classifier for dogs and cats images. The
model has quite well outperformed the centralized one in
performance. A summary of the related work can be found
in Table I.
Paper
Local
Training
Local
Training
Local
Training
Local
Training
Fig. 1. Framework Overview
In order to build healthcare systems that support decentralization and maintain user privacy at the same time, FL and IoT
technologies will be employed in one framework [27]. Also,
deep learning will be used to build robust, high-performance
smart models that require fewer feature engineering processes.
This framework will keep the users’ healthcare data on the
IoT devices that captured this data. Moreover, it will use this
data to train a machine learning model locally. Thus, the data
will be preserved on the devices and only accessible by the
data owners themselves.
Figure 1 shows an overview of the framework and how the
technologies will collaborate with each other. The IoT devices
All participating models will send their updates to an AIready cloud server where these updates will be aggregated to
train the global model. Once the global model training process
is over, each device will receive a new copy of the updated
global model. Accordingly, the models will be trained and
updated constantly without sharing any private data. Thus, the
framework will support IoT-based decentralized architecture
where models will be distributed on IoT devices without
requiring a centralized server to run the model and serve the
users. Moreover, it will maintain privacy by processing and
analyzing users’ data on the IoT devices without sharing it.
106
IV. T RAINING DATA ACQUISITION A LGORITHM
DFL system automation is a huge task as this type of system
deals with a decentralized architecture and each participant
must independently perform their work starting from data
acquisition to using the model for prediction at its best. This
section proposes an algorithm to handle the data acquisition
stage.
According to Algorithm 1, a user is considered to be a
participant in the DFL process after using the previously
shared global model for the detection of skin diseases. When
the user captures an image and uses the model for prediction,
the user will be asked to rate the quality of the prediction on
a scale from 1 to 5. If the rating is higher than 2, the image
and its predicted label will be considered as a training sample
then used in the local training process.
This will ensure offering training samples for the local
training process, as well as checking the quality of the model
prediction over the long term.
Skin diseases
Dataset
Transfer
Learning
Original Model
Device 1 Device 2
FR1
...
Device n-1 Device n
Updates
Model R1
Algorithm 1: Training Data Acquiring
P
TS: (img,label)
Input: F(), img
Output: T S
2 function Dataacquisitionround(F(img))
3
Prediction label=F (img)
4
if (Rating of label > 2 ) then
5
TS ←(img, label)
6
end
7
return T S
8 end
Device 1 Device 2
...
Updates
1
FR4
Updates
Device n-1 Device n
.
.
.
Model R4
Device 1 Device 2
9
...
Device n-1
Device n
Fig. 2. Implementation Process
V. S YSTEM I MPLEMENTATION AND S ETUP
The implemented experiment discusses a skin-disease detection system through images capture by devices’ cameras.
Therefore a Deep learning model was built to detect skin
diseases using transfer learning and to support privacy and
decentralization, the federated learning technique was used on
the same model for four training rounds to update it. Figure
2 illustrates the implementation process in detail.
A. Dataset
Atlas Dermatology dataset [28] was used to build the first
global model. The dataset contains ≈10,000 images for 361
classes. The images was fit into the model in the shape of
224×224×3. Most of the data was used as a training dataset
≈90% and the rest of the data was used as a testing dataset.
B. Original Model and Transfer Learning
Since the dataset was insufficient to build a robust deep
learning model, the dataset used was doubled to increase the
number of samples. Also, the Transfer Learning technique was
used to obtain a good model performance. In the Transfer
Learning phase, one of the Keras applications deep learning
models was used. ResNet50 model was fine-tuned using
the dermatology dataset and compiled over 50 epochs using
ADAM optimizer and learning rate of 0.0001.
C. Federated Learning
As mentioned in Section III, a copy of the global model will
be sent to each participating device in the process, here the FL
process begins. The FL process was conducted for 4 rounds,
each round contains 50 users with 2 samples per user. Initially,
the original skin-diseases detection model will be used as a
global model and its weight will be broadcast to all users.
Then the following steps will be repeated in each Federated
round:
•
•
•
After receiving the global model weights, a local training
process will be initiated on the users’ data separately, then
the models’ weights will be updated.
All models’ weights will be aggregated and averaged.
Update the global model weights using the average of all
local models’ updates.
This phase was conducted using Tensorflow Federated
Learning framework on Google Colab TPU, with 64 GiB
memory. Also, each federated average training round used the
Adam optimizer and the default learning rate of 0.001 for both
client and server.
107
VI. E VALUATION AND R ESULTS
Accuracy
Precision Macro Average
Precision Weighted Average
F1 Macro Average
F1 Weighted Average
Recall Macro Average
Recall Weighted Average
0.94
In this section, we discuss three metrics for evaluation. The
first is the classification report, then the AUC and finally the
QoS.
Percent
0.92
A. Classification Report metrics
Figure 3 illustrates how the FL changed the original model
results for weighted average and macro average of each
classification metric. During the four federated rounds, the
original model’s accuracy decreased from 85% to 1% for
recovery in the final round R4 and up to 85% again. The
macro average percentages for precision, recall, and F1-Score
decreased during federated rounds while the weighted average
for the same metrics fluctuated between increasing the precision up to 87%, a 1% decrease in recall from the original
model then recovery in the fourth round, and maintaining the
same percentage of F1-Score.
B. Area Under The Curve (AUC)
It is a measurement of the model performance at various
threshold settings. The area under the curve ”Receiver Operating Characteristic Curve” shows to what level the model can
distinguish between the data classes. The higher AUC near 1 is
better as the model has a good performance in classifying the
data samples. FL improved the original model AUC as shown
in Figure 4 where the percentage increased up to 97.4% during
the federated rounds.
C. Quality of Service (QoS)
Building systems that are decentralized and maintain privacy is a critical issue because these two factors may affect the
complexity of the system and thus affect the quality of service.
This section will discuss the evaluating of these factors on the
QoS of Federated Learning systems (FL) and Centralized
Learning systems (CL).
Given the Privacy-Preserving (P) factor is not sharing the
users’ personal data, the FL systems will satisfy the factor
while the CL systems do not. Therefore, the FL systems
will have P1 of P factor because they are privacy-preserving
systems as they don’t share personal data, unlike the CL
0.88
0.86
0.84
Original
R1
R2
R3
R4
Fig. 3. Classification report metrics
97.4
97.3
Percent
Classification report is a summary report for main classification metrics that describe the model performance such as:
Precision, Recall, F1-Score and Accuracy.
Accuracy is the percent of how often the model classifies
the samples correctly. As it combines true positives and true
negatives that represent the correctly predicted samples over
all predicted data samples.
Precision is the ratio between true positive predicted samples among all positives samples true and false ones.
Recall is the percentage of true positives that were identified
correctly. It takes the total true positives over the true positives
and the false negatives.
F1-Score is defined as the harmonic mean of the model’s
precision and recall. It used for better evaluation in unbalanced
dataset scenarios.
0.90
97.2
97.1
97.0
96.9
Original
R1
R2
R3
R4
Fig. 4. Models’ AUC
systems that have P0 of P factor as they require sharing users’
data to one cloud storage.
Also, since the FL systems achieve the Decentralization
(D) factor while the CL systems do not, the FL systems will
have D1 as they support the distributed learning technique
between several devices. This is in contrast to CL systems
that will have D0 since they require a centralized database
and server to store the data and run the model.
Taking into account the performed experiment, the FL
model affected the conversion time for one sample and increased it by 1.08 seconds where it was 0.12 in the original
model and became 1.20 on average for the four federated
rounds. Since the original model was built with a centralized
learning technique, the CL model conversion time was less
than the FL model. However, the CL system did not achieve
the D and P factors while the FL system did. Figure 5
illustrates the relationship between these factors.
In conclusion, it is noted that the use of FL improved the
AUC for the original model while maintaining the model’s
accuracy. Also, other classification metrics results such as
accuracy, recall, and F1-Score showed good performance of
models during the federated rounds. Finally, although the FL
system has a higher conversion time which may affect the
quality of service for the user, it achieved the factors of
decentralization and privacy-preserving which the CL system
did not achieve even though its conversion time is less. In our
future work, we expect to continue investigating the following
open issues under this area;
108
FL
1.2
1.0
CT
0.8
0.6
CL
0.4
0.2
P0
P
P1
D0
D
D1
0.0
Fig. 5. Conversion Time vs. Decentralization vs. Privacy-Preserving for FL
system and CT systems
•
•
•
Testing the capabilities of IoT devices: This is important
since there is no minimum standard to run the FL models
on IoT devices.
Evaluate the experiment on a larger dataset: We got initial
results for this due to the finite availability of the skin
diseases dataset, but we need to build and evaluate the
model using a much larger dataset.
Network Capacity and Latency: We believe this will open
new research avenues for both IoT device dropout and
data transmission aspects.
VII. C ONCLUSION
This paper proposed a Deep Federated Learning framework
for decentralized healthcare systems using IoT without sharing
private data thus preserving user privacy. Moreover, an algorithm has been proposed to cover the training data acquiring
stage for the federated averaging toward having a fully automated federated learning process. Furthermore, it described
the implementation of using deep federated learning in the
detection of skin diseases experiment. The results indicated a
higher AUC percentage for the model after federated rounds.
Also, maintaining the accuracy percentage for the model
after federated average as well as good classification metrics
results. Furthermore, achieving not sharing data objective in
the decentralized architecture systems despite increasing the
conversion time of the model.
ACKNOWLEDGEMENT
This work was supported by Qatar University under project
No: IRCC [2020-003]. The findings achieved herein are solely
the responsibility of the authors.
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