1
|
Alghamdi A, Zhu J, Yin G, Shorfuzzaman M, Alsufyani N, Alyami S, Biswas S. Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis. Sensors (Basel) 2022; 22:6786. [PMID: 36146134 PMCID: PMC9501224 DOI: 10.3390/s22186786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/27/2022] [Accepted: 09/01/2022] [Indexed: 06/16/2023]
Abstract
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers' raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users' shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.
Collapse
Affiliation(s)
- Abdullah Alghamdi
- Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Jiang Zhu
- Graduate School, José Rizal University, Mandaluyong 1650, Philippines
| | - Guocai Yin
- School of Computer Science, North China Institute of Aerospace Engineering, Langfang 065099, China
| | - Mohammad Shorfuzzaman
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
| | - Nawal Alsufyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
| | - Sultan Alyami
- Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Sujit Biswas
- Computer Science and Digital Technologies Department, University of East London, London E16 2RD, UK
| |
Collapse
|
2
|
Alvarez-Romero C, Martínez-García A, Sinaci AA, Gencturk M, Méndez E, Hernández-Pérez T, Liperoti R, Angioletti C, Löbe M, Ganapathy N, Deserno TM, Almada M, Costa E, Chronaki C, Cangioli G, Cornet R, Poblador-Plou B, Carmona-Pírez J, Gimeno-Miguel A, Poncel-Falcó A, Prados-Torres A, Kovacevic T, Zaric B, Bokan D, Hromis S, Djekic Malbasa J, Rapallo Fernández C, Velázquez Fernández T, Rochat J, Gaudet-Blavignac C, Lovis C, Weber P, Quintero M, Perez-Perez MM, Ashley K, Horton L, Parra Calderón CL. FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research. Open Res Eur 2022; 2:34. [PMID: 37645268 PMCID: PMC10446092 DOI: 10.12688/openreseurope.14349.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/25/2022] [Indexed: 08/31/2023]
Abstract
Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.
Collapse
Affiliation(s)
- Celia Alvarez-Romero
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain
| | - Alicia Martínez-García
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain
| | - A. Anil Sinaci
- SRDC Software Research Development and Consultancy Corporation, Ankara, 06800, Turkey
| | - Mert Gencturk
- SRDC Software Research Development and Consultancy Corporation, Ankara, 06800, Turkey
| | - Eva Méndez
- Dept. of Library & Inf Sci. Universidad Carlos III de Madrid, Getafe, 28903, Spain
| | - Tony Hernández-Pérez
- Dept. of Library & Inf Sci. Universidad Carlos III de Madrid, Getafe, 28903, Spain
| | - Rosa Liperoti
- Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Roma, 00168, Italy
| | - Carmen Angioletti
- Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Roma, 00168, Italy
| | - Matthias Löbe
- Institute for Medical Informatics (IMISE), University of Leipzig, Leipzig, 04107, Germany
| | - Nagarajan Ganapathy
- PLRI Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany
| | - Thomas M. Deserno
- PLRI Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany
| | - Marta Almada
- Ucibio Requimte, Faculty of Pharmacy University of Porto. Porto4Ageing, Porto, 4050-313, Portugal
| | - Elisio Costa
- Ucibio Requimte, Faculty of Pharmacy University of Porto. Porto4Ageing, Porto, 4050-313, Portugal
| | | | | | - Ronald Cornet
- Amsterdam UMC, University of Amsterdam, Medical Informatics, Amsterdam Public Health, Amsterdam, 1105AZ, The Netherlands
| | - Beatriz Poblador-Plou
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Jonás Carmona-Pírez
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Antonio Gimeno-Miguel
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Antonio Poncel-Falcó
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Aragon Health Service, Zaragoza, 50009, Spain
| | - Alexandra Prados-Torres
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Tomi Kovacevic
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Bojan Zaric
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Darijo Bokan
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Sanja Hromis
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Jelena Djekic Malbasa
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | | | | | - Jessica Rochat
- University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland
| | | | - Christian Lovis
- University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland
| | - Patrick Weber
- Nice Computing SA Le Mont-sur-Lausanne, Le Mont-sur-Lausanne, 1052, Switzerland
| | - Miriam Quintero
- Atos Research and Innovation - ARI. Atos IT., Madrid, 28037, Spain
- Atos Research and Innovation - ARI. Atos Spain., Madrid, 28037, Spain
| | - Manuel M. Perez-Perez
- Atos Research and Innovation - ARI. Atos IT., Madrid, 28037, Spain
- Atos Research and Innovation - ARI. Atos Spain., Madrid, 28037, Spain
| | - Kevin Ashley
- Digital Curation Centre, University of Edinburgh, Argyle House, Edinburgh, EH3 9DR, UK
| | - Laurence Horton
- Digital Curation Centre, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Carlos Luis Parra Calderón
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain
| |
Collapse
|