A Systematic Review on Federated Learning in Medical Image Analysis

Abstract

Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from the beginning of its invention. The eye-catching feature of FL is handling data in a decentralized manner which creates a privacy preserving environment in Artificial Intelligence (AI) applications. As we know medical data includes marginal private information of patients which demands excessive data protection from disclosure to unexpected destinations. In this paper, we performed a Systematic Literature Review (SLR) of published research articles on FL based medical image analysis. Firstly, we have collected articles from different databases followed by PRISMA guidelines, then synthesized data from the selected articles, and finally we provided a comprehensive overview on the topic. In order to do that we extracted core information associated with the implementation of FL in medical imaging from the articles. In our findings we briefly presented characteristics of federated data and models, performance achieved by the models and exclusively results comparison with traditional ML models. In addition, we discussed the open issues and challenges of implementing FL and mentioned our recommendations for future direction of this particular research field. We believe this SLR has successfully summarized the state-of-the-art FL methods for medical image analysis using deep learning.

Full text

Received 2 March 2023, accepted 18 March 2023, date of publication 21 March 2023, date of current version 24 March 2023.
Digital Object Identifier 10.1109/ACCESS.2023.3260027
A Systematic Review on Federated Learning
in Medical Image Analysis
MD FAHIMUZZMAN SOHAN 1AND ANAS BASALAMAH 2
1Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh
2Department of Computer Engineering, Umm Al-Qura University, Mecca 21955, Saudi Arabia
Corresponding author: Md Fahimuzzman Sohan (fahimsohan2@gmail.com)
ABSTRACT Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders
from the beginning of its invention. The eye-catching feature of FL is handling data in a decentralized manner
which creates a privacy preserving environment in Artificial Intelligence (AI) applications. As we know
medical data includes marginal private information of patients which demands excessive data protection from
disclosure to unexpected destinations. In this paper, we performed a Systematic Literature Review (SLR) of
published research articles on FL based medical image analysis. Firstly, we have collected articles from
different databases followed by PRISMA guidelines, then synthesized data from the selected articles, and
finally we provided a comprehensive overview on the topic. In order to do that we extracted core information
associated with the implementation of FL in medical imaging from the articles. In our findings we briefly
presented characteristics of federated data and models, performance achieved by the models and exclusively
results comparison with traditional ML models. In addition, we discussed the open issues and challenges of
implementing FL and mentioned our recommendations for future direction of this particular research field.
We believe this SLR has successfully summarized the state-of-the-art FL methods for medical image analysis
using deep learning.
INDEX TERMS Federated learning, machine learning, medical image analysis, data privacy, systematic
literature review.
I. INTRODUCTION
Image processing and analysis both are different tasks and
often dependent on each other in terms of classifying an
image data. To describe the image processing history we have
to look quite back in 1973, an image of a Swedish model
Lena is the first one that was used for image processing.
Since then image processing has been applied in dozens of
research fields, medical imaging is one of them. An image is
essentially composed of 2D signals (vertical and horizontal),
also with a number of pixels [1]. Different types of images
have their different pixel parameters, during analysis these
parameters help to extract respective information from the
image. On the other hand, the task of the analysis part is
to understand the processed images through different tech-
niques, i.e., Machine Learning (ML); this technique includes
different ML oriented algorithms. At the beginning, classical
The associate editor coordinating the review of this manuscript and
approving it for publication was Sudipta Roy .
ML algorithms (e.g., SVM, naive bayes, decision tree) were
used broadly in image processing research. Later on, it turned
into neural network based modeling after introducing deep
learning and now it is an integral part of any image analysis
task including medical imaging. Every year the usage of
medical imaging increases worldwide for diagnostics. The
image data mainly represents various radiological images
such as, X-ray, computed tomography (CT), magnetic res-
onance imaging (MRI), ophthalmology images, and so on.
Besides, other data from eye, skin, cell have significant con-
tributions in clinical imaging to detect, diagnose and treat dis-
eases [2]. It is becoming increasingly important now to have
these medical images being taken by different devices need
to be sent across from one system to another and therefore
they need a computer network. However, a large collection of
such images creates a dataset, they are located and processed
in cloud servers under ML approach.
In the era of AI, collaborative learning, more specifically
sharing data among different institutions, multiple sources
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
TABLE 1. Contribution of available SLR on FL-driven medical data analysis and out study.
can be very efficient in terms of building robust AI models.
Since models are trained in centralized individual locations
in traditional ML, the collaboration between models is quite
tough. Contrastingly, X-rays, CT, MRI all of these are per-
sonal data pertaining individual patients which need to protect
from risk of this medical information being disclosed or
revealed to any unauthorized third party. In addition, even
though data sharing is possible, the data store, processing,
and analysis are still difficult tasks in a centralized manner.
For such that scenario, data encryption-decryption could be
a potential solution to exchange information between partic-
ipants; however the process could be complex, time consum-
ing and not sustainable [3]. So, instead of bringing the data to
the location where the model is trained, why not bring the
model to the data (institutions and the hospitals) and train
directly there in-house, it allows collaborative learning with-
out centralizing the dataset itself, this is called FL. It was first
introduced in 2016 [4] and gained a lot of attraction within
last couple of years for the healthcare domain. It addresses
the privacy and data protection concern, which is currently
an important problem in developing medical AI. In FL, the
participants can train models locally and estimate different
parameters for respective models, then share the parameters
to a centralized server for aggregating them. Therefore, the
focus is not on which data is used or what algorithms can be
trained, the concept is managing the data in a different way
where data privacy is reserved.
A. OBJECTIVE AND CONTRIBUTION
Since medical images are sensitive data, it needs to be pro-
tected and preserves the rights of users’ personal information.
We already discussed FL is arrived to solve the data privacy
issue in collaborative ML and within the short time the con-
cept has applied in different fields including medical imaging.
Already many articles have been published on FL oriented
medical image analysis and they successfully applied this
unique data management technique in their research articles.
At this stage, it is time to look back, need to review and assess
what has been done till now, what are the impacts of FL on
medical imaging. Meanwhile, some SLR have been published
on the topic, however, they were about overall healthcare
applications not particularly for the medical image analysis
context. A SLR has been presented in [5], they considered all
of the articles which have used all forms of medical data to
train their FL models. Similarly in [3], [9], and [6] the authors
have included the whole healthcare area to survey and review
the papers. Some review articles presented specific medical
domains, for example, Naeem et al. [10] worked particularly
on brain tumor diagnosis using MRI images. Since FL is
comparatively a new concept, most of the review articles
emphasized on the design and implementation. Secondly,
they discussed the privacy or security opportunity, which is
the fundamental characteristic of FL. Some of them [5], [7],
and [10] were formulated on different research questions,
a common question was regarding the state-of-the-art FL
methods; besides, data properties, impact, gaps and future
research have been investigated. Alongside, several survey
articles have been published on FL for healthcare informatics.
Xu et al. [11] surveyed the papers that focus FL in the biomed-
ical area to provide a review. Their effort was to summarize
the privacy, statistical and system challenges that exist in
this specific domain. A well-known article in this field [12],
where the authors discussed the prime factors related to FL
in digital health with challenges and solutions.
This study is a SLR, we exclusively investigated the FL in
medical image analysis and extensively touched every com-
ponent in the considered articles, specially the performance
analysis and comparison with usual ML, which is the main
distinction of our study corresponding to the previously pub-
lished review papers. Our study consisted of several research
questions and by answering the questions we illustrated the
current research lay-out in the field of medical image process-
ing using FL. In addition, several observations were discussed
according to the findings extracted from the literature. Table 1
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
FIGURE 1. Two basic frameworks: working and communication flow of decentralised federated learning in left and usual machine learning in right
for a hospital environment.
shows comparative analysis of our contribution and related
review articles, our study explored the demographic data,
FL architecture, privacy preserving concern, federated data
management, and performance of FL models. We did not find
any article which has worked particularly on medical images.
Consequently, this study can be an outline for future research
of FL application in medical imaging. The following are the
key contributions of our paper:
•We surveyed the insights of FL solely in medical image
research in a systematic way.
•We provided the latest implementation, advancement,
and tendencies toward medical image analysis research
using FL in different aspects.
•We presented and compared the performance of differ-
ent FL architectures used in the reviewed articles with
traditional ML models, which is the first of its kind.
•For incoming contributors we discussed open issues,
challenges, and future direction of the research field.
Rest of the article is structured with six sections. Basic
FL concept is introduced in Section II. Section III described
the procedures of this review. The results of this investi-
gation are presented through different research questions in
Section IV. Open issues and challenges are discussed in
Section V. Besides, Section VI includes the limitation of this
study. Lastly, the conclusion and future directions is provided
in Section VII.
II. FEDERATED LEARNING
In this section we have described an overview of FL architec-
ture. The concept of FL is not related directly to the ML com-
ponents, it is all about a data management process to share
data between multiple clients in a privacy preserving manner.
For a practical example, suppose a hospital environment that
produces some data, also has a model and some computer
resources that would like to tackle a specific problem by an
AI system. Moreover, the dataset in the institution has not
been sufficient to train the model which is able to address
this problem. Another hospital dealing with similar difficulty
wants to work together on this promise where they have a
common goal and can solve a common task. However, both
hospitals have different data locally and they need to use each
other’s data without sharing data directly. This collaborative
model training without sharing the data is exactly the purpose
of FL.
In Fig. 1, we have presented FL in left and traditional ML
framework in right to illustrate the fundamentals of both for
a hospital environment. In association with that, as supple-
mentary information we have listed necessary keywords and
their explanations related to decentralized FL implementation
in Table 2. Since FL consists of multiple sources of data,
we have shown four clients in the figure. Each of the clients
has few common duties, they collect the data from the hospi-
tals, train them using the local ML models and estimate some
parameters. These parameters are sent to the central server
from every client, not the data itself. Once the central server
has received all the local modes’ parameters, it aggregates
them and takes the weighted average, this is known as the
global model and sent back to all of the clients. By this
process a learning round is completed and repeated for the
next round.
However, a well known federated averaging algorithm
is FedAvg [13], proposed by Google in 2016, it calculates
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
TABLE 2. Some commonly used components and their definitions in
federated network.
weighted average of the individual clients. It is very expected
that the data quantity could not be the same across the clients,
sources with larger datasets will have correspondingly larger
weighted losses, individual clients losses are minimized to an
overall global loss which is called weighted average. Under
FedAvg, every client trains a model for a defined number
of epochs through Stochastic Gradient Descent (SGD) algo-
rithm and transmits the learning parameters to the central
server and the server performs aggregation in the form of an
averaging. The mathematical presentation of FedAvg is:
f(w)=
k
X
k=1
nk
nFkw
In this function there are knumber of clients and each client
has its own loss function Fkw. Then weight each of the
losses by the size of the client’s dataset nk. Hence, the overall
objective is to minimize a global loss which is a weighted
combination of local losses and the local loss is computed
on private data which is never shared, only model updates
are shared. Apart from the FedAvg, there are many research
directions and varieties of FL going on such as SecAgg
[54]. Though different combinations exist in the FL imple-
mentation, two characteristics are maintained expectedly: the
datasets are distributed and remain local, not centralized and
have a collaborative model to work towards the same goal.
III. RESEARCH METHOD
There are several review article types available in the litera-
ture to do deeper level of research, such as narrative reviews,
systematic reviews. We mentioned at the beginning that a
systematic review has been conducted for our investigation.
Mainly two SLR methods are popular in practice, one is
PRISMA (Preferred Reporting Items for Systematic Reviews
and Meta-Analyses) and another is Kitchenham’s guidelines;
the second one is mainly considered in computer science
and software engineering research fields [14]. To conduct
this review we followed the PRISMA procedures which is
the most common way of performing SLR in the healthcare
sector [6]. However, for a SLR, first we need to identify
FIGURE 2. The steps taken to conduct this study.
relevant articles that focus on a very specific research area
and question(s), secondly appraising the quality of the studies
performed and the strength of the evidence in the papers, and
lastly synthesize the findings to draw respective conclusions.
Fig. 2shows all of the steps taken to conduct this review
sequentially.
TABLE 3. Formulated research questions of this review.
A. RESEARCH QUESTION
Our first step of this review was to establish a group of ques-
tions which will describe the literature in the most effective
way. Table 3shows the five contexts and their associated
12 research questions. First context is the overview that talked
about the application and problem solved by the FL; next a
broad explanation over the datasets was presented; third ML
framework; then implementation of FL was discussed includ-
ing privacy method, types of FL; and lastly the experimental
substances, specially the performance comparison have been
presented.
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B. SEARCH PROCESS
Since FL was first presented in 2016, the search process of
the review was limited over the time period from 1 January
2017 to 30 June 2022. We discovered all of the common
databases considered by previous researchers; for exam-
ple, Science Direct, IEEE Xplore digital library, Springer
Link, Wiley Online Library, SPIE digital library, ACM dig-
ital library, Multidisciplinary Digital Publishing Institute
(MDPI), Nature Portfolio, Taylor & Francis, and Google
Scholar. The searching criteria is different across the plat-
forms, we used advanced options of each database to search
articles with Boolean ‘‘AND’’ and ‘‘OR’’ expressions. Our
study focused on the implementation of FL in healthcare
image processing, so that we carefully avoided the other
applications. The search phrases looked over the titles,
abstracts, and keywords in each of the databases. Fig. 3
depicts the PRISMA flow diagram where whole statistics of
article consideration in this review has been presented. After
the search operation, primarily collected articles have gone
through a selection process, we have described them in the
next sections.
C. INCLUSION AND EXCLUSION CRITERIA
Literature search strategy is a big challenge while it is needed
to find too many papers, these circumstances are solved by
a predefined inclusion and exclusion criteria in SLR. This
might include limiting the search to only those that contain
certain types of studies. However, the processes ensure the
task achievement properly, reduce the possibility of bias
and protect the selection process from irrelevant research
documents. We implemented the inclusion and exclusion on
the collected articles from the databases to reach the exact
materials that are seeking the readers. We emphasized the
following points to include articles for final analysis:
•Article that studied medical image datasets.
•ML model developed with the FL environment.
•FL was the main focus in the findings (result
analysis/comparison).
Since we performed keyword search, the articles were
collected based on the words present in the paper, even if it
was mentioned for a single time. Therefore, we excluded the
articles that are not relevant and does not fulfill our scope
based on the given criteria:
•Articles that used private dataset(s) for the ML model.
•Studies that are not mainly focused on FL and medical
image data.
•Hybridization or modify the theme of FL, e.g., federated
reinforcement learning.
•Abstract, short article, any pre-print, any book or book
part.
•Articles do not have a clear presentation of the results
using ML based performance measures (e.g., [85], [86]).
The functionalities of inclusion and exclusion are observed
in Fig. 3. It shows the number of initially collected arti-
cles from different databases is 161. We have removed the
duplicate articles from there and 138 articles were taken for
further steps. After that we screened the articles for two times
under two different conditions, first we gently explored the
title and abstract which helps to remove 96 articles, besides,
we extensively investigated the full text of rest 42, where
another 25 papers have been disqualified. Finally, we discov-
ered 17 from 161 articles to hold our review.
D. DATA EXTRACTION
Data collection mostly involved in research questions of our
study, we extracted information in order to cover the ques-
tions perfectly. At first we created a spreadsheet and input
respective information headers on the top. We worked on the
17 articles individually, each time all of the information has
been gathered distinctively on the spreadsheet and they were
used as our findings. The following data are extracted from
every articles:
1) Document title, publication year, and journal/
conference name.
2) Used datasets and their federated settings.
3) The security or privacy protocol used for FL.
4) The algorithms used to train ML models.
5) Performance of the FL model.
IV. RESULTS
We assembled this section following the research questions
that we described in Section III. In the upcoming sections,
first we have presented the demographic analysis (also known
as numerical analysis) data along with the key contributions
and limitations of each reference work in Table 4, thereafter
we answered the 12 questions successively.
A. OVERVIEW
RQ1 What are possible applications of FL?
We found the application of FL in different research fields,
such as, Diabetic Retinopathy (DR), MRI classification, can-
cer, pneumonia, COVID-19 detection, and few more. These
topics are popular in medical image processing research
with conventional ML. Hence, FL also creates new scope to
research due to the privacy production efficiency which is
essential for this particular imaging research.
In 2019, coronavirus disease hitted all over the world
and created a crisis regarding identification of COVID-19
samples. The RT-PCR test is the most reliable diagnosis
method of the diseases, since inadequate testing kits and
some technical limitations, researchers tried to explore alter-
native ways of COVID screening. Therefore, hundreds of
ML based automated and time saving COVID-19 detection
models have been presented within the last two years [33].
ML based COVID analysis is mostly carried out by radi-
ological chest images, i.e., X-ray and CT images. Among
the contributions, FL also discussed and implemented several
detection models as data privacy was a big concern there.
In this study, we found six articles out of 17 were specifically
worked on COVID-19 detection. Feki et al. [18] proposed a
collaborative FL for COVID-19 screening from chest X-ray
images; they cooperated with multiple medical institutions
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TABLE 4. Numerical data of considered articles for this study which include publication year, name of publisher, and data analysis method.
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FIGURE 3. Article consideration process of this review according to PRISMA flow diagram.
without sharing their data. Similarly, Zhang et al. [24] and
Yan et al. [29] used X-ray and CT image data for different
Convolutional Neural Network (CNN) architectures in FL
settings. References [21], [25], and [32] also have contributed
to the COVID-19 infection in a multinational way. However,
during the pandemic such that artificial intelligence tools
were not clinically used significantly to diagnose COVID-19,
all of them were experimental operations and hopefully the
contribution will help in future initiative.
Millions of patients are suffering from fatal diseases world-
wide, cancer is top of them. Researchers have shown early
detection of cancer can save a large number of lives [34].
Consequently, deep learning has emerged as a potential
of early cancer detection by the help of medical images.
It extracts features from the raw images and provides deci-
sions regarding cancer detection with notable performance.
As a part of ML technique, FL has been considered in sev-
eral cancer diagnosis techniques, Fig. 4shows 29.4% arti-
cles (five out of 17) of this review were formed on cancer
detection. Researcher Polap and their team have published
three research papers [17], [19], [22], all of them focused on
skin cancer detection with the FL environment. They used
seven different skin marks (classes) to train the detection
models and successfully implemented the privacy protected
FL. Moreover, Hashmani et al. [30] applied FL on a series of
dermoscopy images to classify nine different skin diseases.
Nowadays, important internal organs of human body, such
as lung, breast cancer are the leading causes of cancer death.
A FL oriented lung cancer detection model has been proposed
by Adnan et al. [28]. They demonstrated that their model
achieved acceptable performance while decentralized data
configuration applied.
One of the domains is Diabetic retinopathy (DR) analysis.
Diabetes is a chronic disease that affects millions of people
globally and uncontrolled diabetes can lead to serious damage
to the body’s system including eyes. DR is a common diabetic
eye disease and the number one cause of vision loss and
blindness in the world. It occurs when diabetes damages
the small blood vessels on the retina. In the primary care
clinic, those retinal images can be transmitted to an eye care
specialist who investigates the image and then provides a
consultation. However, these days deep learning algorithms
can detect the DR within seconds with high accuracy. Lo et
al. [16] analyzed the retinal images to classify the DR posi-
tive and non-DR samples using the FL approach. In another
article, Zhou et al. [31] introduced a FL framework which
classifies five scalability categories of DR, 0 to 4 (No DR
to Proliferative DR).
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TABLE 5. List of dataset used in federated medical imaging with references for quick access.
FIGURE 4. Percentage of FL applied in different diseases diagnosis
research.
Linardos et al. [26] considered FL for Diagnosing Hyper-
trophic Cardiomyopathy (HCM), whether the subjects are
suffering from HMC or normal. In addition to that, a multi-
label cardiac diseases classification has been proposed by
Chakravarty et al. [23], where 14 classes were examined. The
other application includes Autism Spectrum Disorders (ASD)
detection. Li et al. [20] applied deep learning in a FL environ-
ment to classify MRI images. Their model worked for iden-
tifying the ASD using the MRI analysis technique. We also
found FL is used in pneumonia detection, Kaissis et al. [27]
proposed a model that able to detect different pneumonia
samples.
RQ2 What problems were solved?
Almost all of the articles considered in our investigation
solved an universal problem which is ‘ensure the security of
private data’. Data is always a key factor while we need to
train a ML model, besides it is a challenge to protect the data
from potential security and privacy threats. These threats are
more crucial in Electronic Health Record (EHR) data analy-
sis. Sharing EHR data includes patients’ private information,
above all their identity could be under risk to expose publicly.
Similarly in medical image analysis, maintaining privacy of
users’ data such as X-ray, CT, MRI images is going to be
difficult with traditional ML layout. Hence, FL is a privacy
preserving way of training AI algorithms, allows to move the
model to the data rather than moving the data to the model
and this makes it very useful in cases where sensitive data
cannot be shared. Since researchers are working for a long
time on the application domains that we have discussed in the
previous section, now they applied the same fenomena with
privacy preserving FL as their experimental research.
B. DATASET
RQ3 What type of dataset used?
We divided the used datasets in the 17 investigated articles
into several categories based on image type. Data type varies
from model to model, it actually depends on which domain
the model will apply; for example, skin images are used
to detect skin cancer. Fig. 5displays eight different types
of medical images collected from various datasets used in
the research field. Lung X-ray image: As we mentioned,
severe cases of some diseases affect particular organs of
our body, lung is one of them. Literature shows COVID-19
and pneumonia complications include lung damage which is
the reason behind using lung X-ray and CT images in such
disease detection models. Likewise, as we know smoking
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
FIGURE 5. Different types data samples taken from respective dataset. (a) Tuberculosis infected chest X-ray image, (b) COVID-19 positive chest X-ray
image, (c) COVID-19 positive CT image, (d) Brain MRI image for autism spectrum disorders identification, (e) Optical coherence tomography angiography
(OCT-A) image of eye, (f) Light-sensitive tissue data of retinal blood vessel, (g) Skin dermoscopy image, and (h) Lung tissue image for cancer detection.
is dangerous for health, which particularly affects our lungs
and a key reason for lung cancer. According to our inves-
tigation, six articles [18], [23], [25], [27], [29], [32] have
used chest X-ray images out of 17. The X-ray datasets
considered in the articles are Cohen JP, TB x-ray, CheX-
pert, Mendeley data, COVIDx, Chest X-ray (CXR), and
COVID 2019 dataset. Moreover, Zhang et al. [24] proposed
a FL oriented COVID-19 detection model where chest X-ray
and CT images were considered from three datasets, Qatar-
Dhaka data, COVID-CT, and Figure 1dataset. Skin image
data: MNIST: HAM10000 is one of the leading datasets
used in skin cancer detection research with deep learning
techniques. This repository contains 10,015 dermatoscopic
images divided into seven different classes. Połap and the
groups used the dataset in a series of articles [17], [19], [22]
with FL environment. Similar data has been used in [30],
which was released under a dataset challenge competition
called ISIC 2019 and contains 25,331 dermoscopy images.
Retina image data: We found two different articles which
have applied retinal images for their FL models. In [31],
Zhou et al. used a DR dataset consisting of 3,662 images. The
images are noted as five different scalability categories, from
no DR to extreme. Their goal was to classify the different
levels of DR cases. In another article, Lo et al. [16] collected
a total of 153 data samples from four different sources.
Their deep learning model performed binary classification to
define DR and non-DR samples. Others: Adnan et al. [28]
used tissue image data, more specifically they proposed a
privacy guaranteed ML model where lung tissue images were
considered to classify cancer. In addition, Li et al. [20] and
Linardos et al. [26] both used MRI images for their models,
brain and heart MRI data consequently. We have included all
of the dataset name with their references for easy access in
Table 5.
RQ4 Are the number of data samples sufficient?
In ML research, it is very established that the more data
we have for training purposes the better prediction we will
get from the models. Also, chances of model overfitting will
increase when we have a smaller dataset; so, it is always
advisable to use a larger dataset. For our study, we analyzed
the 17 articles by the range of data samples used in the
respective research papers. First, we will discuss the arti-
cles which have used less than 1,000 samples. As Table 5
shows, four papers [16], [18], [20] and [26] used very small
amounts of data, their number of elements are 153, 216, 370,
and 180 respectively. Since larger dataset belongs to better
potentiality of inside analysis, literally 153 data samples are
not technically sound. Next, within the 10,000 sample range,
seven papers [21], [24], [25], [27], [28], [31], and [32] used
data samples between 2,109 and 6,284 and this number is
quite good. Finally, we found six papers [17], [19], [22], [23],
[29], [30] all of them have used more than 10,000 images
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individually. CheXpert, the largest dataset (overall 223,414
CXR image) found under our investigation was considered
by Chakravarty et al. [23].
RQ5 Are non-IID data distribution considered?
There are two forms of federated frameworks exist accord-
ing to the data distribution, IID and non-IID. IID refers to
independent and identically distributed. This can be divided
into two parts, independence and identical distribution; inde-
pendence means that the value (data) of an example does
not affect the value of the other. This particular scenario is
commonly described by a coin flipping experiment, when
a coin is flipped, every time the result of both roles does
not depend on the other die. Identically distributed means
that the probability of any specific outcome is the same, for
example every time flipping a coin there is a 50% chance of
getting heads and a 50% chance of getting tails and that value
does not change while flipping a coin every time. Non-IID
technically inverse from both of the sides. While IID data
feature distribution is same across clients, the feature distribu-
tion is different in non-IID. The problem is quite common in
real life, for example, the appearance of the medical image
sample using different machines across different hospitals
may not align due to different imaging protocols. Therefore,
non-IID data settings mean values are dependent on each
other and there are overall trends between them. Generally
in FL, local models are trained independently where data
distribution is hidden to each other and as a result data type
and features could be vary client to client [6], [53], this
variation makes non-IID data consideration important in FL
research. However, in this study, we investigated FL used
in medical image analysis. We observed FL data structure
is complicated, especially while the local clients’ data are
significantly different to each other. Our results show only
four papers (we did not find sufficient explanation from [26]
and [21]) considered non-IID type along with IID data and
the rest 13 did not talk about the content. In [18], Feki et al.
divided the collected dataset into four parts for clients data,
for IID, they used an equal number of images from both sides,
client and class. Moreover, for non-IID data they allocated
the samples among classes unequally by a ratio of 66% and
44%. Likewise, Adnan et al. [28] performed FL with IID and
non-IID data individually where number of samples were
different in each client under non-IID scenario.
C. ML FRAMEWORK
RQ6 Which ML algorithms are used to train local models?
Although FL is the leading focused topic of this investi-
gation, ML techniques make the actual difference when it
comes to figure out the overall performance of the models.
As usual in the FL framework, each client server data is
trained by ML algorithms. Since our review is based on med-
ical image data and this image analysis or computer vision
task is mostly conducted by CNN oriented deep learning
models. However, to answer the question we searched each
of the considered articles and found a variety of using built-in
TABLE 6. ML and FL methods for medical image analysis.
CNN models, such as VGG16, Inception, ResNet18, and
many more. VGG16 is a widely considered, reliable, and
pre-trained model; five out of 17 surveyed papers considered
this CNN model. This model is constructed by 16 layers,
13 convolutional and 3 fully connected layers. Likewise,
VGG19 is a 19 layers CNN model and used by Lo et al.
[16]. Residual Network (ResNet) is also a commonly used
algorithm that can be constructed by different numbers of
layers, e.g., ResNet18 ([23], [26], [27], [29]), ResNet50 ([18],
[24]), ResNet101 ([24]). Other pre-trained CNN models are
Inception ( [17], [19], [22]), AlexNet ( [17], [19]). Besides,
CNN associated customised deep learning models have been
used in several articles which is listed in Table 6. Li et al. [20]
have used multi-layer perceptron (MLP) classifier which was
a deep neural network constructed by one input, hidden, and
output layers. Adnan et al. [28] performed image segmenta-
tion using a supervised learning approach called Multiple-
Instance Learning (MIL) to train the local models.
D. FL IMPLEMENTATION
RQ7 Are any additional security methods implemented?
Data privacy and security both are not similar in prac-
tice; privacy covers the use (control, access, and regulate)
of data, on the other hand, security defines the poten-
tial threats of unauthorized access and malicious attacks.
FL mainly preserves the privacy concern since trained models
of stakeholders are shared instead of sharing data directly.
Still, sharing models can be vulnerable while parameters are
exchanged between clients and servers and could be a possi-
ble threat against system security [28]. Several additional pri-
vacy preserving methods have been described in a systematic
review article [83]. However, we found few articles that have
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
considered additional initiatives for security in FL based med-
ical imaging research. Most of the articles (three out of four)
have used Differential Privacy (DP), it allows companies to
collect information about their users without compromising
the privacy of an individual and the ultimate goal is to be
able to share information about a dataset with other people
without revealing individuals Personally Identifiable Infor-
mation (PII) from the dataset [9], [84]. Li et al. [20] used
two different mechanisms of DP, Gaussian and Laplace. They
defined the noise level αwhich varied from 0.001 to 1.
Similarly, Kaissis et al. [27] have applied both techniques
and Adnan et al. [28] have used only Gaussian noise in their
experiments. In addition, Połap et al. [17] used encryption
and blockchain techniques to make their FL model more
secure. They proposed three different learning agents where
blockchain technique was applied in Data Management
Agent (DMA). According to their description, all patients
data (images) have to be their unique IDs, once a request
arrive to analysis, it will check whether the ID is exist or not
into the database, if not then it will create an unique ID and a
block to the blockchain, then transfer the ID to the database
with the image.
RQ8 What types federated data partitioning are used?
Mainly three categories of FL described in the previous
literature based on the training data distributions across the
models. Among the three types, Federated Transfer Learn-
ing (FTL) and Vertical FL (VFL) are rarely considered in
medical research; another one, Horizontal FL (HFL) was
used widely. So, in a horizontal partition the client’s database
holds many different customers but they are collecting all the
same type of data on those customers, in other words ‘‘same
features, different samples’’. In vertical FL, it has different
customers in both but there is an overlap of those customers
and they are collecting different features, more specifically
‘‘different features, different samples’’ [3], [9], [84]. How-
ever, in this investigation we focused on the medical image
research and found most of the articles were based on HFL.
For example, Feki et al. [18] utilized HFL, they used a chest
X-ray image dataset where features are same for all clients but
samples are different. Interestingly, Kaissis et al. [27] used
two different datasets for training and testing their FL models,
the fact is both datasets contain X-ray images (same features)
and different data. Only two articles we defined as VFL; [24]
have taken three datasets, two X-ray and one CT image based.
In the article the authors combined the both types of images
and used them to train and test models. In [25], the authors
used X-ray and ultrasound images for their federated models.
X-ray with CT or ultrasound images are technically different,
thus their features will be also different and they used various
data features in different clients which makes a VFL scenario.
RQ9 What are the federated frameworks used?
Table 6represents respective deep learning architectures
that were used for training their local models (we discussed in
RQ6) and next the federated framework which was mainly the
aggression approach of the collected local models in the cen-
tral server. We observed federated mechanisms are executed
in two ways, some articles were driven by formerly proposed
build-in FL algorithms and others with basic concepts for
aggregation. FedAvg (discussed in Section II), which is a
commonly used method in federated aggregation, as Table 6
shows six articles considered this algorithm. Likewise, [20]
and [27] used two different federated algorithms named Fed,
secure aggregation (SecAgg) respectively. SecAgg is a secure
model aggregation for FL also proposed by Google in 2016.
Połap and Woźniak [19] proposed a meta-heuristic search
based federated model, first they calculated average loss of
all local models and then selected only models that have
scored higher than the average loss for aggregation in server.
Mainly all of them pursue fundamental concepts of FL but
they implemented it in different ways. However, the described
above federated aggregation process has no impact on the
model performance, it is all about engineering the data dis-
tribution in a decentralized and collaborative manner.
E. EXPERIMENTAL
RQ10 What are the performance measures used in the
studies?
The final and startling step of any ML setting is to assess
how good the model is through performance evaluation. The
basic idea is to develop a ML model using some training
samples and test this train model on some other unknown
data. However, the training error is not very useful for actual
evaluation, because it is easy to overfit the training data by
using complex models which do not generalize well to future
samples. Contrariwise, testing error is the key metric since
it has a better approximation of the true performance of the
model on future samples. Thereby, we only considered testing
performance throughout our review. As we found from this
investigation, classification and segmentation both tasks were
used and that is why their performance were also evaluated
in different ways. In Fig. 6we have presented the number
of articles using different performance metrics. Most of the
experiments (14 out of 17) were evaluated by accuracy. Recall
was the second commonly used measurement criteria, consid-
ered by five articles. Area Under the Curve (AUC) score three
and precision were used two times.
FIGURE 6. Number of articles applied different performance metrics.
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
RQ11 How is the performance of the FL frameworks
reported?
This question is for getting the overview of performance
achieved by the FL based models in the 17 articles. Perfor-
mance assessment is the ultimate part of any ML model where
the conducted experiment is evaluated by different matrices.
Our investigation revealed 14 articles worked on data classi-
fication (binary and multi-class), one article worked on data
segmentation, and remaining two considered both of them
(listed in Table 4). Usually performance of classification tasks
is assessed by accuracy, it represents the report of correctly
identified samples from all of the data [14]. We divided
the performance into three categories according to the
achieved accuracy by the 17 studies: high (>=90%), medium
(80%-89%), and low (<80%). Table 7summarised the perfor-
mance scores of all articles.
High: We found eight articles have an accuracy of 90% or
more. Feki et al. [18] performed binary classification, their
accuracy score is highest, for FL+VGG16 with data aug-
mentation model 94.4% and for FL+VGG16 93.57%. Połap
and Woźniak [19] used the inception91 classifier for the FL
model and obtained an accuracy of 91%. Score of [25], [30],
and [24] is not clear, they discussed the accuracy between
90-95%. Article [32] and [27] achieved an accuracy of
90.61% and 90% respectively. Yan et al. [29] presented their
results using sensitivity, their highest score was 91.26%.
Medium: In [22], the author classified the images as dis-
eases and not a disease, their proposed VGG based FL model
achieved 89.82% accuracy. Lo et al. [16] performed classi-
fication and segmentation both tasks on different datasets,
the classification and segmentation accuracy for SFU dataset
were 88% and 85% respectively, classification accuracy of
OHSU dataset was 89%. In [26], Linardos et al. considered
AUC, the highest score achieved by the FL model was 89%.
Adnan et al. [28] conducted binary classification with an
accuracy of 85%.
Low: The rest three articles performed multi-class classi-
fication, where Chakravarty et al. [23] 14, Połap et al. [17]
seven, and Li et al. [20] have considered four classes, their
acquired performances are AUC 80%, accuracy 70% and
76% respectively.
RQ12 How perform the FL approach compared to the
conventional models?
Last research question explores the comparative perfor-
mance analysis between FL and traditional ML image pro-
cessing research. This query is important while we want to
discuss the effect, contribution, and drawback of using FL in
medical image analysis. To answer this question we inten-
sively collected experiment results from both areas, 17 FL
articles and their relevant conventional models. We already
described the performance of the FL models in the previous
question and here we will present the results of usual ML
models and then the comparative analysis. In Table 7we sum-
marized the performance of all articles in this review and we
presented the results of one or more similar articles opposite
to each of the articles to make a comparison chart. To do
TABLE 7. Performance (accuracy) comparison, the results of each of the
reviewed articles and their respective compatible ML models with
references.
so, we extensively investigated dozens of research papers
that analyzed medical images by traditional ML to explore
best matching options which was essential for a reliable
comparison. Several conditions were applied in this criteria
based on the structural and experimental similarity between
ML and FL papers, such as we considered the papers which
used similar datasets, algorithms, and performance measures.
We expect maintaining this condition will ensure an accurate
comparison among the two parties. Our investigation shows
in Table 7that all of the ML models have improved accuracy
compared to their respective FL models in existing literature,
more specifically we found better ML results against every
FL article. For instance, Połap et al. [17] have achieved accu-
racy with federated VGG16 70% and Inception 67%, how-
ever in ML part, Jain et al. [56] achieved 79.23% accuracy
with Inception and Liu et al. [57] 87% with ResNet50; all
of three have considered the MNIST: HAM10000 dataset.
Then as well Chakravarty et al. [23] has an AUC score of
80% with FL environment, but with same dataset and ML
algorithm article [67] and [68] have 86% and 87% AUC
respectively.
V. OPEN ISSUES AND CHALLENGES
FL is still a young research field, so it is difficult to draw
a remark on the rejection and acceptance. However, here
we have discussed the issues and challenges found in the
reviewed articles regarding the application of FL in medical
image. Generally, FL is invented to fulfill the privacy concern
of private data, unfortunately it does not cover all potential
privacy threats [93]. However, we described model perfor-
mance, data heterogeneity, and federated model efficiency
issues found from the review below:
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
A. PRIVACY AND SECURITY
Medical image data is created by personal information of
patients and no one can share this data for AI applications
without reliable data protection. FL makes the data sharing
between the different institutions with some privacy guar-
antees by an advanced data management and model con-
struction process, all we have described in Section II. FL is
different compared to ML models where the training process
is exposed to multiple parties, we do not know the motive
of every participant, it is an issue of trust among them; so
this additional communication increases the risk of leakage
data via reverse engineering. Meanwhile, we observed two
further privacy measures used in federated medical image
processing, differential privacy and secure aggregation. Dif-
ferential privacy involves adding carefully selected noise to
the outputs and can either be done by the individual clients or
server level, secure aggregation is a cryptographic technique
(e.g., blockchain technology), ensures the server can only see
the aggregate of thousands of updates rather than individual
model updates. But the reality is every privacy mechanism
comes with a significant computational cost on the federation.
B. DATA HETEROGENEITY
Our investigation shows data heterogeneity could occur in
two ways: number of samples are different (non-IID data) and
data features are different (VFL) among the clients. Usually,
the number of produced data in hospitals are not identical and
in FL, clients can have different data distributions, this uneven
distribution of data of client sides might provide opposing
gradient updates to the server which is challenging to tackle.
Furthermore, practically features of federated datasets are not
the same in many cases, for instance X-ray and CT images
data can be used in two different clients which makes trouble
during aggregate the models parameters centrally in a FL
setting.
C. OVERALL MODEL PERFORMANCE
The first impression of an AI model is the performance, how
accurately the model accomplished the task. High perfor-
mance accuracy makes the model more acceptable than a
model that achieved a lower score. We previously discussed
the federated model performance and compared them with
traditional ML models (RQ12). Our findings show FL failed
to perform better than ML with similar model structures, this
drawback claims us to reevaluate the usefulness of FL in
medical image.
D. FEDERATED ARCHITECTURE
Training a personalized model on each of the clients is not
difficult in FL, problems emerge when all of the model
output transfers to the central server and passes through an
aggregation process. We observed that the federated models
presented in the reviewed articles are mostly theoretical and
less practically implemented, few articles included their open
source code with their articles. Since the research started in
the field a couple of years ago, the research method and mate-
rials need to be more easily accessible to future researchers.
Besides, we usually have a very controlled setting in research,
but the question comes when we try to aim for huge datasets
to simulate in a real-world scenario.
VI. LIMITATIONS
In this section we have admitted the limitations of this study.
First, we searched all prominent databases for article collec-
tion where some journals and conference proceedings were
with subscription download policy. In some of such cases,
we could not grab the papers from the sources. Although,
we tried for an alternative way, sent email to the correspond-
ing authors and requested for a full text of the required article.
However, still we failed to reach some of them ( [94] and
[95]) which is limiting the range of this survey. In addition,
our inclusion and exclusion process removed articles from
the initial fleet and preprint articles were not included there,
besides we could not explore all of the searching databases
so it could be possible that we missed to include any relevant
article(s) on the topic. We did not experiment the models used
in the 17 articles under our supervision, for a precise review
that would have been more effective. Overall, it is difficult
to conclude this study with strong and tested historical evi-
dence, because our review was on very limited time and with
insufficient resources since FL was recently introduced.
VII. CONCLUSION AND FUTURE DIRECTIONS
One of the most popular and effective diagnosis methods
is imaging techniques in the medical sector. This practice
is increasing day by day and produces tons of image data.
AI has lots of opportunities in medical imaging using this
data, but clinical use of AI and ML is very limited right now.
In research direction, creating a publicly shareable image
dataset is very difficult for the medical domain. The major
hurdle behind data share and collaboration is privacy issues
which are less prioritized in typical centralized models. Apart
from this concept, federated or distributed learning is differ-
ent, here a data-driven learning model is shared not the data
directly. In this study, we systematically reviewed the articles
that considered FL in their ML based medical image research.
We elaborately discussed from every perspective, including
demographic data, privacy appearance, datasets, FL charac-
teristics, model implementation, and performance compar-
ison. We noticed in one of our previous articles [33] that
deep learning oriented COVID-19 detection using X-ray and
CT images has high accuracy, most of them achieved more
than 95% accuracy. We further observed a similar trend in
this study, here COVID-19 detection research articles are the
top scorers with FL mechanism. Although, the scores under
FL are comparatively lower than general models, as listed in
Table 7. Performance of other application domains with FL
models were also not mentionable. Besides, previous articles
point out the implementation of federated models is relatively
complex, it requires extra communication and maintenance
trouble. However, it is favorable to become acquainted that
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
TABLE 8. Quality questions and the scores achived by the 17 articles.
the research field got lots of attention and publications within
a very little time, that is why we can hope for promising
progress of FL in medical image analysis in future. At this
stage, we have summarized our findings below for future
direction to the researchers who are interested to contribute
in the field:
•Privacy concern is not fully solved in FL, however,
we cannot deny the importance of decentralized con-
cepts. It could be effective for collaborative ML in med-
ical image research, thus researchers should emphasize
on the implementation of additional privacy protection
in a cost effective way.
•Datasets in the research are collected from various
sources and for various purposes where experimental
results could differ enormously. There is no particu-
lar or benchmark dataset available in federated med-
ical imaging research; need to build some standard
datasets to avoid biased data and data heterogeneity
problems.
•Similarly no benchmark FL model has been presented
yet in this field, such that initiative will assist to build
robust AL models for further research.
•In truth, collaborative models data are prone to be het-
erogeneous, various classes of data are collaborating
there. But our results show the accuracy of multi-class
classification is very low (as described in RQ11) which
needs to be addressed in future research.
•Federated models achieved satisfactory performance in
some cases but we cannot narrate as an alternative in the
accuracy race with ML models.
•There are many weaknesses observed in current publi-
cations (papers investigated in this review) of this field,
we included the article quality checklist and results in
A. Future research could consider the quality analysis
questionnaires for article quality improvement.
No doubt FL is something that might be in the future horizon.
But still there are some technical problems, that challenges
need to be tackled before FL is going to be applied vastly.
Best of our knowledge this is the first SLR and we believe
this review is a reflection of FL research in the area of medical
imaging.
APPENDIX
QUALITY ANALYSIS
Table 8shows 12 Quality Questions (QQ) and scores, mostly
motivated from our previous article [14]. The goal of such
inquiry was to check the basic quality of the articles published
in FL oriented medical imaging. However, each question
has one score for one article and a total score of 12 for an
individual. We considered the QQ answer in three forms of
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M. F. Sohan, A. Basalamah: Systematic Review on FL in Medical Image Analysis
scoring, ‘‘Yes (1)’’, ‘‘Partially Yes (0)’’, and ‘‘No (−1)’’. The
article which clearly supports the question is Yes, partially
supported or where no clear answer found is Partially Yes,
and lastly fully disagreed is No. We investigated each of
the articles to find the answer and assigned the scores in
respective columns. As the table interprets most of the articles
have failed to fulfill the quality requirement. Highest score is
9 out of 12 gained by [27], followed by six for [20] and [28]
both articles individually. The score indicates in some areas
quality has been maintained poorly in the research papers,
a reason could be that lots of attention made a rush on FL
research among the contributors.
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MD FAHIMUZZMAN SOHAN received the
B.Sc. degree in software engineering from
Daffodil International University, Bangladesh,
in 2019. He has published several papers in reputed
journals and conferences. His research interests
include machine learning, computer vision, and
image processing.
ANAS BASALAMAH received the M.Sc. and
Ph.D. degrees from Waseda University, Tokyo, in
2006 and 2009, respectively. He was a Postdoc-
toral Researcher with The University of Tokyo and
the University of Minnesota, in 2010 and 2011,
respectively. He is currently an Associate Profes-
sor with the Department of Computer Engineering,
Umm Al-Qura University. His research interests
include embedded networked sensing, smart cities,
ubiquitous computing, participatory, and urban
sensing.
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