Agent architecture of an intelligent medical system based on federated learning and blockchain technology
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Polap and their team published three research papers [17], [19], [22], all focused on skin cancer detection with FL.
“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.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
Połap and the groups used the MNIST: HAM10000 dataset in a series of articles [17], [19], [22] with FL environment.
“Połap and the groups used the dataset in a series of articles [17], [19], [22] with FL environment.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
Six reviewed papers [17], [19], [22], [23], [29], [30] used more than 10,000 images individually.
“Finally, we found six papers [17], [19], [22], [23], [29], [30] all of them have used more than 10,000 images individually.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
Inception was used in [17], [19], and [22]; AlexNet in [17] and [19].
“Other pre-trained CNN models are Inception ( [17], [19], [22]), AlexNet ( [17], [19]).”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
Polap et al. [17] used encryption and blockchain techniques to make their FL model more secure, proposing three learning agents with blockchain in the Data Management Agent (DMA).
“In addition, Polap 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).”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
Połap et al. [17] achieved 70% accuracy with federated VGG16 and 67% with Inception, while Jain et al. [56] achieved 79.23% with Inception and Liu et al. [57] 87% with ResNet50 on the MNIST: HAM10000 dataset.
“For instance, Połap et al. [17] have achieved accuracy with federated VGG16 70% and Inception 67%, however 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.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
The remaining three articles performed multi-class classification: Chakravarty et al. [23] used 14 classes, Połap et al. [17] seven classes, and Li et al. [20] four classes; their performances were AUC 80%, accuracy 70%, and 76%, respectively.
“Low: The rest three articles performed multi-class classification, 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.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
In Połap et al. [17], the accuracy is low.
“The accuracy is low”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
Połap et al. [17] used blockchain-based additional privacy for federated models.
“Blockchain based additional privacy used”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
In Połap et al. [17], non-IID data is not considered.
“non-IID”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
Połap et al. [17] used AlexNet, VGG16, and Inception, and Basic FL as the federated algorithm.
“[17] | AlexNet, VGG16, and Inception | Basic FL”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [17]
The proposed security architecture achieved an accuracy of 80% for 25 iterations.
“the proposed architecture achieved an accuracy of 80 % for 25 iteration.”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [99]
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Self-citation
- Authors
- Połap, D., Srivastava, G., & Yu, K.
- Affiliations
- Silesian University of Technology (PL)
- China Medical University (TW)
- Waseda University (JP)
- Corresponding author
- Srivastava, Gautam
- Venue
- Journal of Information Security and Applications
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 58
- Pages
- 102748
- ISSN
- 2214-2126 (ISSN-L)
- OpenAlex
- W3127000387
- Cited by
- 106
- References
- 39
- Citations by year
- 2025: 11 · 2024: 22 · 2023: 27 · 2022: 28 · 2021: 18
- FWCI
- 21.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Information Security and Applications (publishedVersion)
- Funders
- Politechnika Śląska (09/010/RGJ21/0057)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- multi-agent systems, federated learning, blockchain, Internet of Medical Things, medical data privacy, data security, ensemble classification, agent architecture
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