Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
<p dir="ltr">Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge comput...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513560964497408 |
|---|---|
| author | Adnan Qayyum (16875936) |
| author2 | Kashif Ahmad (12592762) Muhammad Ahtazaz Ahsan (16904673) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| author2_role | author author author author |
| author_facet | Adnan Qayyum (16875936) Kashif Ahmad (12592762) Muhammad Ahtazaz Ahsan (16904673) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| author_role | author |
| dc.creator.none.fl_str_mv | Adnan Qayyum (16875936) Kashif Ahmad (12592762) Muhammad Ahtazaz Ahsan (16904673) Ala Al-Fuqaha (4434340) Junaid Qadir (16494902) |
| dc.date.none.fl_str_mv | 2022-09-14T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcs.2022.3206407 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Collaborative_Federated_Learning_for_Healthcare_Multi-Modal_COVID-19_Diagnosis_at_the_Edge/24056352 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Data management and data science Machine learning COVID-19 Computed tomography Medical services X-ray imaging Feature extraction Collaborative work Data models Distributed computing Machine learning Smart healthcare |
| dc.title.none.fl_str_mv | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, has gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by evaluating the potential of intelligent processing of clinical data at the edge. We utilized the emerging concept of clustered federated learning (CFL) for an automatic COVID-19 diagnosis. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific image modality) are trained with central data, and improvements of 16% and 11% in overall F1-Scores have been achieved over the trained model trained (using multi-modal COVID-19 data) in the CFL setup on X-ray and Ultrasound datasets, respectively. We also discussed the associated challenges, technologies, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Computer Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcs.2022.3206407" target="_blank">https://dx.doi.org/10.1109/ojcs.2022.3206407</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a2f3c3ffb009357aab8eec92567696da |
| identifier_str_mv | 10.1109/ojcs.2022.3206407 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056352 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the EdgeAdnan Qayyum (16875936)Kashif Ahmad (12592762)Muhammad Ahtazaz Ahsan (16904673)Ala Al-Fuqaha (4434340)Junaid Qadir (16494902)EngineeringBiomedical engineeringInformation and computing sciencesData management and data scienceMachine learningCOVID-19Computed tomographyMedical servicesX-ray imagingFeature extractionCollaborative workData modelsDistributed computingMachine learningSmart healthcare<p dir="ltr">Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, has gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by evaluating the potential of intelligent processing of clinical data at the edge. We utilized the emerging concept of clustered federated learning (CFL) for an automatic COVID-19 diagnosis. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific image modality) are trained with central data, and improvements of 16% and 11% in overall F1-Scores have been achieved over the trained model trained (using multi-modal COVID-19 data) in the CFL setup on X-ray and Ultrasound datasets, respectively. We also discussed the associated challenges, technologies, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Computer Society<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcs.2022.3206407" target="_blank">https://dx.doi.org/10.1109/ojcs.2022.3206407</a></p>2022-09-14T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcs.2022.3206407https://figshare.com/articles/journal_contribution/Collaborative_Federated_Learning_for_Healthcare_Multi-Modal_COVID-19_Diagnosis_at_the_Edge/24056352CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240563522022-09-14T00:00:00Z |
| spellingShingle | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge Adnan Qayyum (16875936) Engineering Biomedical engineering Information and computing sciences Data management and data science Machine learning COVID-19 Computed tomography Medical services X-ray imaging Feature extraction Collaborative work Data models Distributed computing Machine learning Smart healthcare |
| status_str | publishedVersion |
| title | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge |
| title_full | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge |
| title_fullStr | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge |
| title_full_unstemmed | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge |
| title_short | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge |
| title_sort | Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge |
| topic | Engineering Biomedical engineering Information and computing sciences Data management and data science Machine learning COVID-19 Computed tomography Medical services X-ray imaging Feature extraction Collaborative work Data models Distributed computing Machine learning Smart healthcare |