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1.
18th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA) ; 2021.
Article in English | Web of Science | ID: covidwho-1799378

ABSTRACT

Sensitive patient data is generated from a variety of sources and then transferred to a cloud for processing. Therefore, it is exposed to security and privacy and may lead to an increase in communication costs. Edge computing will ease computing pressure through distributed computational capabilities while improving security and privacy. In this paper, we propose a Federated PSN (FPSN) model where the model is moved directly to the edge to minimize computation and communication costs. PSN has been applied as a successful approach in categorizing and diagnosing patients based on similarities against some clinical and non-clinical features. Our proposed model distributes processing at each edge node, then fuses the constructed PSN matrices at the cloud premises, which significantly reduce the model's training and inference time and ensures quick model updates with the local client/nodes. In this paper, we propose: (i) an algorithm to evaluate patient's data similarity at the edge;and (ii) an algorithm to implement the federated similarity network fusion at the Cloud. We conducted a set of experiments to evaluate our FPSN model against other machine learning algorithms using a COVID-19 dataset. The results obtained prove that the FPSN model accuracy is higher than the distributed PSNs at various edges and higher than the accuracies of other classification models.

2.
24th International Conference on Advanced Communication Technology, ICACT 2022 ; 2022-February:109-112, 2022.
Article in English | Scopus | ID: covidwho-1789855

ABSTRACT

For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage. © 2022 Global IT Research Institute-GiRI.

3.
2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 ; : 96-103, 2022.
Article in English | Scopus | ID: covidwho-1788620

ABSTRACT

As the COVID19 pandemic evolves and coronavirus mutates to different variants, a high workload falls on the shoulders of doctors and radiologists. Identifying COVID19 through X-ray and Computed Tomography (CT) scanning in a short amount of time is vital because it helps doctors start the COVID19 treatment in the early stages. Deep Learning algorithms showed tremendous results in automating COVID19 detection using X-ray and CT scans. As there are not many survey papers on COVID19 detection using deep learning techniques, the goal of this paper is (1) to give a thorough discussion of COVID19 prediction considering Computer Vision problems like COVID19/pneumonia classification, detection, and segmentation, (2) to address new advances in deep learning like Transformers, GANs, and LSTMs, and (3) to cover technical issues like data security and data scarcity of X-ray and CT scans in COVID19. © 2022 IEEE.

4.
12th International Conference on Applications and Technologies in Information Security, ATIS 2021 ; 1554 CCIS:21-36, 2022.
Article in English | Scopus | ID: covidwho-1772872

ABSTRACT

During the COVID-19 pandemic, artificial intelligence (AI) plays a major role to detect and distinguish between several lungs diseases and diagnose COVID-19 cases accurately. This article studies the feasibility of the federated learning (FL) approach for identifying and distinguishing COVID-19 X-ray images. We trained and tested FL components by using the data sets that collect images of three different lungs conditions, COVID-19, common lungs and viral pneumonia. We develop and evaluate FL model horizontally with same parameters and compare the performance with the classic CNN model and the transfer learning approaches. We found that FL can quickly train artificial intelligence models on different devices during a pandemic, avoiding privacy leaks that may be caused by such a high resolution personal and private X-ray data. © 2022, Springer Nature Singapore Pte Ltd.

5.
2021 IEEE Globecom Workshops, GC Wkshps 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1746089

ABSTRACT

The Internet of Medical Things (IoMT) is a set of medical devices and applications that connect to healthcare systems through the Internet. Those devices are equipped with communication technologies that allow them to communicate with each other and the Internet. Reliance on the IoMT is increasing with the increase in epidemics and chronic diseases such as COVID-19 and diabetes;with the increase in the number of IoMT users and the need for electronic data sharing and virtual services, cyberattacks in the healthcare sector for accessing confidential patient data has been increasing in the recent years. The healthcare applications and their infrastructures have special requirements for handling sensitive users' data and the need for high availability. Therefore, securing healthcare applications and data has attracted special attention from both industry and researchers. In this paper, we propose a Federated Transfer Learning-based Intrusion Detection System (IDS) to secure the patient's healthcare-connected devices. The model uses Deep Neural Network (DNN) algorithm for training the network and transferring the knowledge from the connected edge models to build an aggregated global model and customizing it for each one of the connected edge devices without exposing data privacy. CICIDS2017 dataset has been used to evaluate the performance in terms of accuracy, detection rate, and average training time. In addition to preserving data privacy of edge devices and achieving better performance, our comparison indicates that the proposed model can be generalized better and learns incrementally compared to other baseline ML/DL algorithms used in the traditional centralized learning schemes. © 2021 IEEE.

6.
18th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2021 ; 2021-December, 2021.
Article in English | Scopus | ID: covidwho-1735776

ABSTRACT

Sensitive patient data is generated from a variety of sources and then transferred to a cloud for processing. Therefore, it is exposed to security and privacy and may lead to an increase in communication costs. Edge computing will ease computing pressure through distributed computational capabilities while improving security and privacy. In this paper, we propose a Federated PSN (FPSN) model where the model is moved directly to the edge to minimize computation and communication costs. PSN has been applied as a successful approach in categorizing and diagnosing patients based on similarities against some clinical and non-clinical features. Our proposed model distributes processing at each edge node, then fuses the constructed PSN matrices at the cloud premises, which significantly reduce the model's training and inference time and ensures quick model updates with the local client/nodes. In this paper, we propose: (i) an algorithm to evaluate patient's data similarity at the edge;and (ii) an algorithm to implement the federated similarity network fusion at the Cloud. We conducted a set of experiments to evaluate our FPSN model against other machine learning algorithms using a COVID-19 dataset. The results obtained prove that the FPSN model accuracy is higher than the distributed PSNs at various edges and higher than the accuracies of other classification models. © 2021 IEEE.

7.
21st IEEE International Conference on Data Mining, ICDM 2021 ; 2021-December:1102-1107, 2021.
Article in English | Scopus | ID: covidwho-1722911

ABSTRACT

Federated learning (FL) has emerged as a promising privacy-aware paradigm that allows multiple clients to jointly train a model without sharing their private data. Recently, many studies have shown that FL is vulnerable to membership inference attacks (MIAs) that can distinguish the training members of the given model from the non-members. However, existing MIAs ignore the source of a training member, i.e., the information of the client owning the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients. The leakage of source information can lead to severe privacy issues. For example, identification of the hospital contributing to the training of an FL model for the COVID-19 pandemic can render the owner of a data record from this hospital more prone to discrimination if the hospital is in a high risk region. In this paper, we propose a new inference attack called source inference attack (SIA), which can derive an optimal estimation of the source of a training member. Specifically, we innovatively adopt the Bayesian perspective to demonstrate that an honest-but-curious server can launch an SIA to steal non-trivial source information of the training members without violating the FL protocol. The server leverages the prediction loss of local models on the training members to achieve the attack effectively and non-intrusively. We conduct extensive experiments on one synthetic and five real datasets to evaluate the key factors in an SIA, and the results show the efficacy of the proposed source inference attack. © 2021 IEEE.

8.
7th International Conference on Engineering and Emerging Technologies, ICEET 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1704971

ABSTRACT

Pneumonia Detection has been a real problem for the last few centuries. Detecting Pneumonia has been a job for the skilled, such as doctors and medical practitioners. Visiting doctors in this time in many countries is very tough with Covid-19 on the rise and stricter lockdown regulations. Deep Learning has helped build many systems and algorithms over the years to detect pneumonia using X-ray images. Such Deep Learning models are first trained on many X-ray images that would be collected from multiple hospitals and diagnostic centers and then can be deployed centrally for people to use them. However, building such models is impeded by the problem of garnering mass data from hospitals due to data confidentiality between patients and hospitals. For that, we propose a system where detecting Pneumonia would be done using a Deep Learning model with a Federated Learning approach and achieve an accuracy of around 90%. This will build a central model by training local models in different hospitals with their own data, maintaining all patient data privacy. © 2021 IEEE.

9.
IEEE Transactions on Industrial Informatics ; 2022.
Article in English | Scopus | ID: covidwho-1699483

ABSTRACT

The rapidly increasing volume of user credit-related data generated by connected devices in the Industrial Internet of Things (IIoT) paradigm opens up new possibilities for improving the quality of service for emerging applications through credit data sharing. However, security and privacy issues (such as credit data leakage) are significant barriers to credit data providers and applications sharing their data in wireless networks. Leakage of private credit data can lead to serious problems, not only in terms of financial loss for the data provider, but also in terms of illegal use of personal credit data. In particular, the economic recovery after the global COVID-19 pandemic has further boosted the demand for efficient, secure credit models for Industry 4.0, which could alleviate the potential credit crisis under financial pressure. IEEE

10.
40th IEEE/ACM International Conference on Computer Aided Design (ICCAD) ; 2021.
Article in English | Web of Science | ID: covidwho-1691675

ABSTRACT

The outbreak of the global COVID-19 pandemic emphasizes the importance of collaborative drug discovery for high effectiveness;however, due to the stringent data regulation, data privacy becomes an imminent issue needing to be addressed to enable collaborative drug discovery. In addition to the data privacy issue, the efficiency of drug discovery is another key objective since infectious diseases spread exponentially and effectively conducting drug discovery could save lives. Advanced Artificial Intelligence (AI) techniques are promising to solve these problems: (1) Federated Learning (FL) is born to keep data privacy while learning data from distributed clients;(2) graph neural network (GNN) can extract structural properties of molecules whose underlying architecture is the connected atoms;and (3) generative adversarial network (GAN) can generate novel molecules while retaining the properties learned from the training data. In this work, we make the first attempt to build a holistic collaborative and privacy-preserving FL framework, namely FL-DISCO, which integrates GAN and GNN to generate molecular graphs. Experimental results demonstrate the effectiveness of FL-DISCO on: (1) IID data for ESOL and QM9, where FL-DISCO can generate highly novel compounds with high drug-likeliness, uniqueness and LogP scores compared to the baseline;(2) non-IID data for ESOL and QM9, where FL-DISCO generates 100% novel compounds with high validity and LogP scores compared to the baseline. We also demonstrate how different fractions of clients, generator and discriminator architectures affect our evaluation scores.

11.
Comput Netw ; 205: 108672, 2022 Mar 14.
Article in English | MEDLINE | ID: covidwho-1683021

ABSTRACT

The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial-terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach.

12.
Intelligent Systems with Applications ; : 200064, 2022.
Article in English | ScienceDirect | ID: covidwho-1627154

ABSTRACT

In recent years, as economic stability is shaking, and the unemployment rate is growing high due to the COVID-19 effect, assigning credit scoring by predicting consumers’ financial conditions has become more crucial. The conventional machine learning (ML) and deep learning approaches need to share customer’s sensitive information with an external credit bureau to generate a prediction model that opens up the door of privacy leakage. A recently invented privacy-preserving distributed ML scheme referred to as Federated learning (FL) enables generating a target model without sharing local information through on-device model training on edge resources. In this paper, we propose an FL-based application to predict customers’ financial issues by constructing a global learning model that is evolved based on the local models of the distributed agents. The local models are generated by the network agents using their on-device data and local resources. We used the FL concept because the learning strategy does not require sharing any data with the server or any other agent that ensures the preservation of customers’ sensitive data. To that end, we enable partial works from the weak agents that eliminate the issue if the model convergence is retarded due to straggler agents. We also leverage asynchronous FL that cut off the extra waiting time during global model generation. We simulated the performance of our FL model considering a popular dataset, Give me Some Credit (Freshcorn, 2017). We evaluated our proposed method considering a a different number of stragglers and setting up various computational tasks (e.g., local epoch, batch size), and simulated the training loss and testing accuracy of the prediction model. Finally, we compared the F1-score of our proposed model with the existing centralized and decentralized approaches. Our results show that our proposed model achieves an almost identical F1-score as like centralized model even when we set up a skew-level of more than 80% and outperforms the state-of-the-art FL models by obtaining an average of 5∼6% higher accuracy when we have resource-constrained agents within a learning environment.

13.
IEEE Transactions on Industrial Informatics ; 2021.
Article in English | Scopus | ID: covidwho-1621801

ABSTRACT

Deep learning has demonstrated its efficacy and potential to solve challenging computer vision problems in medical and other industrial applications. Federated learning is a learning paradigm that facilitates collaborative learning in a federation of users without exchanging actual data with a single authority like a server. However, federated learning provides only a basic level of privacy and robustness and is vulnerable to model poisoning and model inversion attacks in hostile training environments. Hence, we propose MediSecFed-a secure framework for federated learning in a hostile environment. Compared to the widely used FedAvg, our method relies on simple and practical ideas from knowledge distillation and model inversion to ensure additional security and privacy features. Our approach achieves knowledge exchange among participating entities without sharing model parameters as FedAvg does, thus protecting the privacy of the local data from the server and significantly reducing communication costs. We evaluate our method on two chest X-ray datasets. Our method outperforms FedAvg by 15% on both datasets in a hostile environment. Our method will also continue to maintain good performance even if the number of malicious participating entities increases. Robustness to learn in a malicious environment while preserving privacy with reduced communication costs makes our method more desirable and efficient than that of FedAvg. IEEE

14.
Ieee Internet of Things Journal ; 8(21):15884-15891, 2021.
Article in English | Web of Science | ID: covidwho-1570217

ABSTRACT

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients' privacy concerns. This causes the issue of insufficient data sets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients' local data. Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists. To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections. First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images. Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients' training time. In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis. The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance.

15.
IEEE International Conference on Communications (ICC) ; 2021.
Article in English | Web of Science | ID: covidwho-1559788

ABSTRACT

To combat the novel coronavirus (COVID-19) spread, the adoption of technologies including the Internet of Things (IoT) and deep learning is on the rise. However, the seamless integration of IoT devices and deep learning models for radiograph detection to identify the presence of glass opacities and other features in the lung is yet to be envisioned. Moreover, the privacy issue of the collected radiograph data and other health data of the patients has also arisen much concern. To address these challenges, in this paper, we envision a federated learning model for COVID-19 prediction from radiograph images acquired by an X-ray device within a mobile and deployable screening resource booth node (RBN). Our envisioned model permits the privacy-preservation of the acquired radiograph by performing localized learning. We further customize the proposed federated learning model by asynchronously updating the shallow and deep model parameters so that precious communication bandwidth can be spared. Based on a real dataset, the effectiveness of our envisioned approach is demonstrated and compared with baseline methods.

16.
International Journal of Intelligent Systems ; 2021.
Article in English | Wiley | ID: covidwho-1557796

ABSTRACT

The coronavirus of 2019 (COVID-19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID-19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID-19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID-19 detection using chest X-ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID-19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.

17.
Soft comput ; : 1-12, 2021 Nov 18.
Article in English | MEDLINE | ID: covidwho-1525537

ABSTRACT

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods.

18.
Procedia Comput Sci ; 192: 3711-3721, 2021.
Article in English | MEDLINE | ID: covidwho-1461762

ABSTRACT

Infectious diseases accompanied mankind throughout its existence. However, in the 20th century, with the implementation od mass vaccination, this problem was partially forgotten. It reappeared at the end of the 2019 with the COVID-19 pandemic. The diseases are associated with high mortality, the main causes of which are: respiratory failure, acute respiratory distress syndrome, thrombotic complications, etc. As many centuries ago, the key to fighting a pandemic is to diagnose patients with infections as quickly as possible, isolate them, and implement treatment procedures. In this paper we propose a Platform supporting medics in the fight against epidemic. Unlike alternative systems, the proposed IT Platform will ultimately cover all areas of fighting against COVID-19, from the diagnosis of infection, through treatment, to rehabilitation of post-disease complications. Like most clinical information systems, the Platform is based on Artificial Intelligence, in particular Federated Learning. Also, unlike known solutions, it uses all available historical data of the patient's health and information from real-time mobile diagnostics, using cellular communication and Internet of Things solutions. Such solutions could be helpful in fighting against any future mass infections.

19.
Healthcare (Basel) ; 9(8)2021 Aug 08.
Article in English | MEDLINE | ID: covidwho-1348622

ABSTRACT

The world is facing multiple healthcare challenges because of the emergence of the COVID-19 (coronavirus) pandemic. The pandemic has exposed the limitations of handling public healthcare emergencies using existing digital healthcare technologies. Thus, the COVID-19 situation has forced research institutes and countries to rethink healthcare delivery solutions to ensure continuity of services while people stay at home and practice social distancing. Recently, several researchers have focused on disruptive technologies, such as blockchain and artificial intelligence (AI), to improve the digital healthcare workflow during COVID-19. Blockchain could combat pandemics by enabling decentralized healthcare data sharing, protecting users' privacy, providing data empowerment, and ensuring reliable data management during outbreak tracking. In addition, AI provides intelligent computer-aided solutions by analyzing a patient's medical images and symptoms caused by coronavirus for efficient treatments, future outbreak prediction, and drug manufacturing. Integrating both blockchain and AI could transform the existing healthcare ecosystem by democratizing and optimizing clinical workflows. In this article, we begin with an overview of digital healthcare services and problems that have arisen during the COVID-19 pandemic. Next, we conceptually propose a decentralized, patient-centric healthcare framework based on blockchain and AI to mitigate COVID-19 challenges. Then, we explore the significant applications of integrated blockchain and AI technologies to augment existing public healthcare strategies for tackling COVID-19. Finally, we highlight the challenges and implications for future research within a patient-centric paradigm.

20.
Sensors (Basel) ; 21(15)2021 Jul 24.
Article in English | MEDLINE | ID: covidwho-1325761

ABSTRACT

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.


Subject(s)
COVID-19 , Internet of Things , Humans , Lung/diagnostic imaging , Radiography , SARS-CoV-2 , Supervised Machine Learning
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