Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems

Kalapaaking, Aditya Pribadi; Khalil, Ibrahim; Yi, Xun

doi:10.1109/tetc.2023.3268186

Cited by 125Privacy-Preserving Technologies in DataBlockchain Technology Applications and SecurityAdversarial Robustness in Machine Learning

Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems

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Kalapaaking, Aditya Pribadi, Khalil, Ibrahim, Yi, Xun·2024·IEEE Transactions on Emerging Topics in Computing

DOI 10.1109/tetc.2023.3268186

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Abstract

Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL) has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based industries, and smart cities. FL enables clients to train a global model collaboratively without accessing their local training data. However, the current FL schemes are vulnerable to adversarial attacks. Its architecture makes detecting and defending against malicious model updates difficult. In addition, most recent studies to detect FL from malicious updates while maintaining the model's privacy have not been sufficiently explored. This paper proposed blockchain-based federated learning with SMPC model verification against poisoning attacks for healthcare systems. First, we check the machine learning model from the FL participants through an encrypted inference process and remove the compromised model. Once the participants' local models have been verified, the models are sent to the blockchain node to be securely aggregated. We conducted several experiments with different medical datasets to evaluate our proposed framework.

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SMPC allows edge devices to perform computations on their local models while maintaining privacy.
“SMPC allows edge devices to perform computations on their local models while maintaining privacy.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
In SMPC, each edge device contributes its local model without revealing the model parameters or the training data.
“In SMPC, each edge device i contributes its local model w i without revealing the parameters of the model or the data used for training.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
Secure protocols such as homomorphic encryption or secret sharing facilitate computation of aggregate statistics, including gradients, on encrypted data.
“Secure protocols such as homomorphic encryption or secret sharing facilitate the computation of aggregate statistics, including gradients, on encrypted data.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
Secure aggregation methods aggregate updates without disclosing sensitive information to the central server or other parties, thus maintaining privacy.
“These methods aggregate updates without disclosing sensitive information to the central server or other parties, thus maintaining privacy.”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]
SMPC ensures that individual edge device data remain secret throughout the aggregation process, making it suitable for scenarios with strict privacy requirements.
“SMPC ensures that individual edge device data remain secret throughout the aggregation process, making it suitable for scenarios with strict privacy requirements [84].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [84]

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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400 · see citing works
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Nasr, M., Shokri, R., & Houmansadr, A. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00065 · Cited by 1,760 · see citing works
On the Convergence of FedAvg on Non-IID Data
Li, X., Huang, K., Yang, W., Wang, S., & Zhang, Z. (2019) · arXiv · doi:10.48550/arxiv.1907.02189 · Cited by 1,575 · see citing works
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495 · see citing works
A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480 · see citing works
Data Poisoning Attacks Against Federated Learning Systems
Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58951-6_24 · Cited by 927 · see citing works
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv · doi:10.48550/arxiv.1911.11815 · Cited by 828 · see citing works
Federated-Learning-Based Anomaly Detection for IoT Security Attacks
Mothukuri, V., Khare, P., Parizi, R. M., Pouriyeh, S., Dehghantanha, A., & Srivastava, G. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3077803 · Cited by 727 · see citing works
Python Programming Language.
van Rossum, G. (2006) · USENIX Annual Technical Conference · Cited by 579 · see citing works
BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning
Zhang, C., Li, S., XIA, J., Wang, W., Yan, F., & Liu, Y. (2020) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 522 · see citing works
Stealing Hyperparameters in Machine Learning
Wang, B., & Gong, N. Z. (2018) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2018.00038 · Cited by 491 · see citing works
Local Differential Privacy-Based Federated Learning for Internet of Things
Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037194 · Cited by 456 · see citing works
Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing
Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2977383 · Cited by 451 · see citing works
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
Yang, J., Shi, R., & Ni, B. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434062 · Cited by 414 · see citing works
Privacy-Enhanced Federated Learning Against Poisoning Adversaries
Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., & Lu, R. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3108434 · Cited by 351 · cited 1× · see citing works
“In [17], the author proposed a privacy-enhanced FL against poisoning adversaries.”
Background
VeriFL: Communication-Efficient and Fast Verifiable Aggregation for Federated Learning
Guo, X., Liu, Z., Li, J., Gao, J., Hou, B., Dong, C., & Baker, T. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3043139 · Cited by 319 · see citing works
PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems
Zhang, J., Chen, B., Cheng, X., Binh, H. T. T., & Yu, S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3023126 · Cited by 291 · see citing works
Toward Accurate Anomaly Detection in Industrial Internet of Things Using Hierarchical Federated Learning
Wang, X., Garg, S., Lin, H., Hu, J. W., Kaddoum, G., Piran, M. J., & Hossain, M. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3074382 · Cited by 269 · see citing works
PySyft: A Library for Easy Federated Learning
Ziller, A., Trask, A., Lopardo, A., Szymkow, B., Wagner, B., Bluemke, E., Nounahon, J.-M., Passerat-Palmbach, J., Prakash, K., Rose, N., et al. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-70604-3_5 · Cited by 246 · see citing works
A Framework for Evaluating Client Privacy Leakages in Federated Learning
Wei, W., Liu, L., Loper, M. L., Chow, K.-H., Gürsoy, M. E., Truex, S., & Wu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58951-6_27 · Cited by 236 · see citing works
Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application
Sun, L., Jiang, X., Ren, H., & Guo, Y. M. (2020) · IEEE Access · doi:10.1109/access.2020.2997831 · Cited by 228 · see citing works
VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain Systems
Peng, Z., Xu, J., Chu, X., Gao, S., Yao, Y., Gu, R., & Tang, Y. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3050781 · Cited by 219 · see citing works
Anonymous and Privacy-Preserving Federated Learning With Industrial Big Data
Zhao, B., Fan, K., Yang, K., Wang, Z., Li, H., & Yang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3052183 · Cited by 169 · see citing works
Research on Disease Prediction Based on Improved DeepFM and IoMT
Yu, Z., Amin, S. U., Alhussein, M. A., & Lv, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3062687 · Cited by 105 · see citing works
Distributed privacy preserving k-means clustering with additive secret sharing
Doğanay, M. C., Pedersen, T. B., Saygın, Y., Savaş, E., & Levi, A. W. (2008) · doi:10.1145/1379287.1379291 · Cited by 88 · see citing works
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Kumar, R., Khan, A. A., Kumar, J., Zakria, Golilarz, N. A., Zhang, S., Ting, Y., Zheng, C., & Wang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076767 · Cited by 57 · see citing works

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Pati, S., Kumar, S., Varma, A., Edwards, B., Lu, C., Qu, L., Wang, J. J., Lakshminarayanan, A., Wang, S.-H., Sheller, M., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.100974 · Cited by 269 · see citing works
From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare
Li, M., Xu, P., Hu, J., Tang, Z., & Yang, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103497 · Cited by 143 · see citing works
Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings
Haripriya, R., Khare, N., & Pandey, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-97565-4 · Cited by 137 · see citing works
Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions
Badidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
Privacy-preserving in Blockchain-based Federated Learning systems
Sameera, K. M., Nicolazzo, S., Arazzi, M., Nocera, A., A., R. R. K., Vinod, P., & Conti, M. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.04.024 · Cited by 104 · see citing works
Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review
Bathula, A., Gupta, S. K., Merugu, S., Saba, L., Khanna, N. N., Laird, J. R., Sanagala, S. S., Singh, R. K., Garg, D., Fouda, M. M., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10873-5 · Cited by 100 · see citing works
Green Federated Learning: A New Era of Green Aware AI
Thakur, D., Guzzo, A., Fortino, G., & Piccialli, F. (2025) · ACM Computing Surveys · doi:10.1145/3718363 · Cited by 77 · see citing works
Integrating Post-Quantum Cryptography and Blockchain to Secure Low-Cost IoT Devices
Castiglione, A., Esposito, J. G., Loia, V., Nappi, M., Pero, C., & Polsinelli, M. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3485796 · Cited by 72 · see citing works
Federated learning for green and sustainable 6G IIoT applications
Khanh, Q. V., Nguyen, D. C., Anh, D. V., & Quý, N. M. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101061 · Cited by 66 · see citing works
A survey on federated learning for security and privacy in healthcare applications
Coelho, K. K., Nogueira, M., Vieira, A. B., Silva, E. F., & Nacif, J. A. M. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.05.012 · Cited by 64 · see citing works
A Game Theory-Based Incentive Mechanism for Collaborative Security of Federated Learning in Energy Blockchain Environment
He, Y., Luo, M., Wu, B. L., Sun, L., Wu, Y., Liu, Z., & Xiao, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3282732 · Cited by 49 · see citing works
Safeguarding Patient Data-Sharing: Blockchain-Enabled Federated Learning in Medical Diagnostics
Myrzashova, R., Alsamhi, S. H., Hawbani, A., Curry, E., Guizani, M., & Wei, X. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3409329 · Cited by 49 · see citing works
A Survey on Blockchain-Based Federated Learning
Wu, L., Ruan, W., Hu, J., & He, Y. (2023) · Future Internet · doi:10.3390/fi15120400 · Cited by 48 · see citing works
Defending Against Poisoning Attacks in Federated Learning With Blockchain
Dong, N., Wang, Z., Sun, J., Kampffmeyer, M., Knottenbelt, W. J., & Xing, E. P. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3376651 · Cited by 47 · see citing works
Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning
Nagamani, G. M., & Kumar, C. K. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41071 · Cited by 46 · see citing works
Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review
Svandova, K., & Smutný, Z. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s459987 · Cited by 45 · see citing works
Blockchain-Empowered Federated Learning: Benefits, Challenges, and Solutions
Cai, Z., Chen, J., Fan, Y., Zheng, Z., & Li, K. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3541560 · Cited by 40 · see citing works
Blockchain Enabled Smart Healthcare System Using Jellyfish Search Optimization With Dual-Pathway Deep Convolutional Neural Network
Alruwaili, F. F., Alabduallah, B. I., Alqahtani, H., Salama, A. S., Mohammed, G. P., & Alneil, A. A. (2023) · IEEE Access · doi:10.1109/access.2023.3304269 · Cited by 37 · see citing works
PD2S: A Privacy-Preserving Differentiated Data Sharing Scheme Based on Blockchain and Federated Learning
Liu, Y., Liu, P., Jing, W., & Song, H. H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3295763 · Cited by 36 · see citing works
Blockchain-Enabled Federated Learning in Healthcare: Survey and State-of-the-Art
Nezhadsistani, N., Moayedian, N. S., & Stiller, B. (2025) · IEEE Access · doi:10.1109/access.2025.3587345 · Cited by 36 · see citing works
Defending Against Data Poisoning Attack in Federated Learning With Non-IID Data
Yin, C., & Zeng, Q. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3296885 · Cited by 34 · see citing works
A privacy-enhanced framework for collaborative Big Data analysis in healthcare using adaptive federated learning aggregation
Haripriya, R., Khare, N., Pandey, M., & Biswas, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01169-8 · Cited by 32 · see citing works
Building the Smart City of Tomorrow: A Bibliometric Analysis of Artificial Intelligence in Urbanization
Karger, E., Rothweiler, A., Brée, T., & Ahlemann, F. (2025) · Urban Science · doi:10.3390/urbansci9040132 · Cited by 30 · see citing works
Unleashing the prospective of blockchain-federated learning fusion for IoT security: A comprehensive review
Gupta, M., Kumar, M., & Dhir, R. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100685 · Cited by 27 · see citing works
FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality
Yan, B., Zhang, H., Xu, M., Yu, D., & Cheng, X. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3353455 · Cited by 24 · see citing works
DAQFL: Dynamic Aggregation Quantum Federated Learning Algorithm for Intelligent Diagnosis in Internet of Medical Things
Qu, Z., Zhao, X., Sun, L., & Muhammad, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3537614 · Cited by 24 · see citing works
Toward Decentralized Intelligence: A Systematic Literature Review of Blockchain-Enabled AI Systems
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Qureshi, R., & Koo, I. (2026) · Applied Sciences · doi:10.3390/app16031511 · Cited by 24 · see citing works
Breast Cancer Prediction Using Shapely and Game Theory in Federated Learning Environment
Supriya, Y., & Chengoden, R. (2024) · IEEE Access · doi:10.1109/access.2024.3424934 · Cited by 20 · see citing works
Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review
Wang, X., Xie, Y., Chen, X., Yang, J., Li, R., Gao, W., Yan, Z., Zhou, H., & Ye, Z. (2026) · Journal of Medical Internet Research · doi:10.2196/79052 · Cited by 18 · see citing works
Federated Learning and Blockchain: A Cross-Domain Convergence
Sharma, S., Pandey, A. K., Sharma, V., Mishra, S., & Alkhayyat, A. H. (2023) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs59847.2023.10390227 · Cited by 18 · see citing works
A systematic review of innovations for real-time image security in IoT applications using machine learning and blockchain
Rai, M., Kumar, S., & Rathore, P. S. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02535-8 · Cited by 16 · see citing works
State of the art and taxonomy survey on federated learning and blockchain integration in UAV applications
Alaya, H., Letaïfa, A. B., & Rachedi, A. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07124-x · Cited by 16 · see citing works
Towards blockchain-enabled decentralized and secure federated learning
Ma, X., Xu, D., & Wolter, K. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120368 · Cited by 15 · see citing works
Decentralized Federated Learning for Private Smart Healthcare: A Survey
Cheng, H., Qu, Y., Liu, W., Gao, L., & Zhu, T. (2025) · Mathematics · doi:10.3390/math13081296 · Cited by 14 · see citing works
Federated learning and blockchain approach for securing IoT data
Wankhede, S. B., & Patel, D. (2025) · Discover Internet of Things · doi:10.1007/s43926-025-00234-1 · Cited by 13 · see citing works
A Privacy-Preserving Federated Learning Method with Homomorphic Encryption for Chronic Kidney Disease Stage Prediction
Gayathri Hegde, M., Ruthvika, B., Jain, R. B., Shenoy, P. D., Venugopal, K. R., & Canchi, A. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.11928 · Cited by 13 · see citing works
A Comprehensive Review of Cryptographic Techniques in Federated Learning for Secure Data Sharing and Applications
Sen, A., Heng, S., & Tan, S. C. (2025) · IEEE Access · doi:10.1109/access.2025.3593953 · Cited by 12 · see citing works
Federated Learning for Sentiment Analysis in Presence of Non-IID Data: Sensitivity of Deep Learning Models
Gholamiangonabadi, D., & Grolinger, K. (2024) · IEEE Access · doi:10.1109/access.2024.3453068 · Cited by 12 · see citing works
An asynchronous federated learning-assisted data sharing method for medical blockchain
Gan, C., Xiao, X., Zhang, Y., Zhu, Q., Bi, J., Jain, D. K., & Saini, A. (2024) · Applied Intelligence · doi:10.1007/s10489-024-06172-9 · Cited by 12 · see citing works
Exploring Federated Learning Tendencies Using a Semantic Keyword Clustering Approach
Enguix, F., Carrascosa, C., & Rincon, J. A. (2024) · Information · doi:10.3390/info15070379 · Cited by 11 · see citing works
A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference
Selvakumar, S., & Senthilkumar, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-07622-1 · Cited by 10 · see citing works
Secure and Efficient Federated Learning Against Model Poisoning Attacks in Horizontal and Vertical Data Partitioning
Yu, C., Meng, Z., Zhang, W., Lei, L., Ni, J., Zhang, K., & Hai, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3486028 · Cited by 9 · see citing works
VFL-Chain: Bulletproofing Federated Learning in the V2X environments
Smahi, A., Li, H., Wang, H., Fateh, A. A., & Chan, C. C. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.02.012 · Cited by 9 · see citing works
A Survey of Attacks on Blockchain Systems Using a Layer-based Approach
Das, J., Tasin, S. A. A., Rabbi, M. F., & Ferdous, M. S. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111274 · Cited by 7 · see citing works
Blockchain-based Approaches for Secure Federated Learning
de Souza, L. A. C., Camilo, G. F., Rebello, G. A. F., Guimarães, L. C. B., Campista, M. E. M., Costa, L. H. M. K., & Duarte, O. C. M. B. (2024) · Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS) · doi:10.1109/brains63024.2024.10732504 · Cited by 7 · see citing works
Hierarchical Federated Learning-Based Method for privacy-preserving in the Healthcare Environment
P, M. K. D., Jayachandra, A. B., NagaSireesha, J., Venkatesh, B., & Siddiqua, A. (2023) · International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) · doi:10.1109/easct59475.2023.10393636 · Cited by 6 · see citing works
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Guo, P., Xu, S., & Liang, W. (2025) · Intelligent Computing · doi:10.34133/icomputing.0132 · Cited by 6 · see citing works
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Roy, S., & Bera, D. (2023) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants59832.2023.10469165 · Cited by 6 · see citing works
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Haripriya, R., Khare, N., Pandey, M., & Biswas, S. (2025) · Cluster Computing · doi:10.1007/s10586-025-05290-4 · Cited by 5 · see citing works
Generative AI-based intrusion detection systems for intra-vehicle networks
Asaouer, G., & Boubiche, D. E. (2025) · Ad Hoc Networks · doi:10.1016/j.adhoc.2025.104031 · Cited by 5 · see citing works
Blockchain for smart healthcare: A systematic review of security, interoperability, and AI–IoT integration
Abbas, S. R., Abbas, Z., Rehman, M. U., & Lee, S. W. (2026) · Digital Health · doi:10.1177/20552076261420985 · Cited by 5 · see citing works
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Parmadi, B. D., & Ramli, K. (2025) · International Journal of Electrical Computer and Biomedical Engineering · doi:10.62146/ijecbe.v3i1.112 · Cited by 4 · see citing works
Enhancing Trust and Reliability in AI and ML: Assessing Blockchain's Potential to Ensure Data Integrity and Security
Siddika, A., & Zhao, L. (2023) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy59711.2023.10361446 · Cited by 4 · see citing works
Blockchain Sharding based Federated Learning Empowered with NFT-based Privacy-Preserving Model Sharing
Khemaissia, R., Derdour, M., & Djeddai, A. (2024) · International Conference on Pattern Analysis and Intelligent Systems (PAIS) · doi:10.1109/pais62114.2024.10541291 · Cited by 4 · see citing works
FedCon: Scalable and Efficient Federated Learning via Contribution-Based Aggregation
Gao, W., Xu, G., & Meng, X. (2025) · Electronics · doi:10.3390/electronics14051024 · Cited by 4 · see citing works
Federated Learning in Smart Cities
Mirmahaleh, S. Y. H., & Rahmani, A. M. (2024) · Wiley eBooks · doi:10.1002/9781394219230.ch18 · Cited by 4 · see citing works
Teleportation Links: Mitigating Catastrophic Forgetting in Decentralized Federated Learning
Wang, X., Chen, Y., Ye, Q. J., & Dobre, O. A. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3611109 · Cited by 3 · see citing works
HyperShield: A personalized hypernetwork-based defense against poisoning attacks in federated learning for healthcare
Thuong, D. B., Duc, M. V., An, V. T., Thanh, N. H., Tran, K. P., & Hương, T. T. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130237 · Cited by 3 · see citing works
Thwarting gradient inversion in federated learning via generative shadow mapping defense
Zhou, H., Chen, Y., Qin, Z., Deng, X., & Peng, Z. (2025) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2025.103671 · Cited by 3 · see citing works
Beyond vulnerabilities: A comprehensive survey of adversarial attacks across domains
Asimopoulos, D. C., Grammatikis, P. I. R., Papadopoulos, G. T., & Sarigiannidis, P. G. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100963 · Cited by 3 · see citing works
Privacy-Preserving Healthcare Analytics in Indonesia Using Lightweight Blockchain and Federated Learning: Current Landscape and Open Challenges
Mardiansyah, V., Bayuaji, L., Herlistiono, I. O., Violina, S., Purnama, A., Prasetyo, B. A., & Huynh, P.-H. (2025) · Indonesian Journal of Electronics Electromedical Engineering and Medical Informatics · doi:10.35882/ijeeemi.v7i2.63 · Cited by 3 · see citing works
Secure federated learning in the Internet of Health Things for improved patient privacy and data security
Kalinaki, K., Alli, A. A., Asuman, B., & Zakari, R. Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00003-5 · Cited by 3 · see citing works
Enhancing AI Security: A Review of Blockchain-Integrated Solutions
Wei, C. (2024) · International Conference on Computer Science and Blockchain (CCSB) · doi:10.1109/ccsb63463.2024.10735521 · Cited by 3 · see citing works
A Blockchain-Based Federated-Learning Framework for Defense against Backdoor Attacks
Lü, L., Qin, J., & Luo, J. (2023) · Electronics · doi:10.3390/electronics12112500 · Cited by 3 · see citing works
A Review of Federated Learning Attacks: Threat Models and Defence Strategies
Zakaria, F., & Khalid, S. K. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160754 · Cited by 2 · see citing works
Dual-driven synergy of blockchain and federated learning for trustworthy medical data sharing in internet of medical things
Gan, C., Tan, X., Zhu, Q., Saini, A., Jain, D. K., & Diro, A. A. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104348 · Cited by 2 · see citing works
DADM2D-SFL: Decentralised Aggregation framework based on DBSCAN Malicious Model Detection for Secure Federated Learning
Ailane, A., Bourekkache, S., Atia, O. B., Samara, M. A., Hamani, N., Kahloul, L., & Lorenz, P. (2026) · Computer Networks · doi:10.1016/j.comnet.2025.111987 · Cited by 2 · see citing works
Systematic Review of Privacy Preservation in Federated Learning for Secured Healthcare Applications
Alankamony, A., & Nels, N. (2026) · Information · doi:10.3390/info17070647 · Cited by 2 · see citing works
Transforming Conflict Response with IoT: Innovations, Challenges, and Ethical Considerations in Humanitarian Aid Delivery
Parmadi, B. D., & Ramlib, K. (2025) · Devotion Journal of Research and Community Service · doi:10.59188/devotion.v6i4.25450 · Cited by 2 · see citing works
Blockchain-Based Federated Learning-Convolutional Neural Network for Preserving Data Privacy and Security
P, M. K. D., Ananda Babu., J., Lakshmi, C. B. S., Fatima, T., & Rajesh, N. (2023) · International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) · doi:10.1109/easct59475.2023.10393069 · Cited by 2 · see citing works
Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things
Reddy, N. V. R. S., Swathi, B., Kumari, P. M. K., Chintamaneni, P., Naick, B. R., & Pradhan, B. G. (2024) · Journal of Integrated Science and Technology · doi:10.62110/sciencein.jist.2024.v12.805 · Cited by 1 · see citing works
Fed-RWM: A Robust Watermarking Approach for Federated Learning Model Ownership Protection
Tong, X., Li, S., Jin, N., & Cui, B. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3596692 · Cited by 1 · see citing works
Distributing trust: A P2P SMPC protocol for secure federated learning
Demetrakopoulos, P., Anagnostopoulos, T., & Tsekeridou, S. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11390879 · Cited by 1 · see citing works
Adversarial Model Poisoning in CNNs for Brain Tumor Detection: Risks to Smart Healthcare Integrity
Singh, P., Lata, K., & Saini, S. (2025) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit66168.2025.11400258 · Cited by 1 · see citing works
False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions
Edelson, M., Pham, A., & Kuo, T.-T. (2025) · Scientific Reports · doi:10.1038/s41598-025-04069-2 · Cited by 1 · see citing works
Architectural patterns for blockchain-enabled federated learning: A systematic literature review
Wang, J., Shi, D., Li, S., Yan, J., Wang, Y., Mao, Q., Liu, Y., Zhu, L., & Zhang, H. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112695 · Cited by 1 · see citing works
Dynamic Generator: A Stealth-Preserving Generator-Based Data Poisoning Attack in Federated Learning With Defensive Countermeasures in IoT Systems
Liao, X., Wu, Z., He, B., Zhao, H., Shang, S., Wu, D., & Su, C. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3645273 · Cited by 1 · see citing works
FedSecure: A Robust Federated Learning Framework for Adaptive Anomaly Detection and Poisoning Attack Mitigation in IoMT
Alruwaili, F. J., Mohanty, S. P., & Kougianos, E. (2025) · IEEE International Conference on Secure IoT, Assured and Trusted Computing (SATC) · doi:10.1109/satc65530.2025.11137301 · Cited by 1 · see citing works
A fully decentralized federated adversarial vision transformer with blockchain and secure aggregation for visual-based intrusion and malware forensics
Belal, M. M., Saleti, S., & Elakkiya, E. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-01011-7 · Cited by 1 · see citing works
Personalized and sustainable federated learning in integrated Internet of Things
Zheng, X., Zheng, Y., Wang, T., Mu, C., Yan, K., & Tian, L. (2026) · Digital Communications and Networks · doi:10.1016/j.dcan.2026.01.001 · Cited by 1 · see citing works
BDFL: a blockchain-based decentralized federated learning security framework
Shi, K., Xue, T., Wang, J., Wu, Y., & Guo, H. (2026) · Multimedia Systems · doi:10.1007/s00530-025-02148-y · Cited by 1 · see citing works
Blockchain-Enabled Secure Federated Learning for Electronic Medical Records: A Scalable and Privacy-Preserving Framework
Indra Yatini, B., Iriyanta, K., Hendra, S.P., B. T. S., Muhrial, H., & Prayitno, E. (2025) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti68345.2025.11393406 · Cited by 1 · see citing works
Blockchain-Based Personalized Federated Learning Leveraging the SMPC protocol
Dhumale, S., Ameria, D., & Shukla, S. K. (2023) · IEEE International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/iccst59048.2023.10726855 · Cited by 1 · see citing works
Research Directions for Decentralized Technology Transactions: An Update
Hadi, A., Rahmadika, S., Rahmi, U., Larasati, H. T., & Fajri, B. R. (2025) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt20252161 · Cited by 1 · see citing works
A Deep Learning Approach for Prediction of Heart Disease using Ensemble Learning TechniquesPreprint
Hayat, M., Javaid, N., Nazir, A., Alrajeh, N. A., Shafiq, M., & Choi, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4526000/v1 · Cited by 1 · see citing works
Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges
Cheng, H., Liu, W., Qu, Y., Fu, K., & Wang, Q. (2024) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc63484.2024.00015
SecureFed: Blockchain-Based Defence for Data Poisoning Attack in Federated Learning
Alsharidah, A. A., Jha, D. N., Wei, B. W., Solaiman, E., & Ranjan, R. K. (2024) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc63386.2024.00064
A Survey of Next-Generation AI and Its Evolving LandscapePreprint
Khan, A., Amjad, H., Hameed, R., Amjad, H., Hameed, R., Bilal, M. A., Fazal, D. S., Rehman, A., Chohan, S. N., & Munib, S. (2025) · doi:10.36227/techrxiv.175329122.29020847/v1
PoFQ: a blockchain consensus protocol for decentralized federated learning-based threat hunting approach in a trustless computing landscape
Hieu, V. T. T., Quyen, N. H., Hoang, H. D., Duy, P. T., & Pham, V.-H. (2025) · Cluster Computing · doi:10.1007/s10586-025-05247-7
Leveraging Blockchain and Federated Learning for Smart Cities
Gupta, U., Rawat, G. S., Singh, J. V., & Jain, A. (2025) · Wiley eBooks · doi:10.1002/9781394167760.ch9
Enhancing the Privacy and Security of Federated Learning: A Survey
Wang, D., & Lu, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-09694-4_21
Byzantine-Resilient Federated Deep Learning for Secure and Privacy-Preserving Smart Healthcare Using Krum
Shakir, I. A., Yaseen, W. Y., Shalal, A. A., Salman, D. S., Ahmed, A. A., Ibrahim, H. S., Awad, A. S., & Rasool, Z. H. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292446
Federated Learning and Blockchain for Real-Time Disease Monitoring in Healthcare 4.0 Networks
Choubey, S., Gupta, K., Singh, S., & Sharma, S. (2025) · International Conference on Sustainability, Innovation & Technology (ICSIT) · doi:10.1109/icsit65336.2025.11295175
SPAD – A Secure and Privacy-Preserving Distributed Analytics Framework
Makhdoom, I., Abolhasan, M., Lipman, J., Franklin, D. R., & Piccardi, M. (2025) · IEEE International Conference on Blockchain and Cryptocurrency (ICBC) · doi:10.1109/icbc64466.2025.11114453
Decentralising AI: Can Blockchain Technology Solve Data Privacy Challenges in Machine Learning?
Murphy, D. G., Lundie, A., Sun, R., & Wilkinson, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-7121-2_4
A Survey of Next-Generation AI and Its Evolving LandscapePreprint
Khan, A., Amjad, H., Amjad, H., Amjad, H., Hameed, R., Bilal, M. A., Fazal, D. S., Rehman, A., Chohan, S. N., & Munib, S. (2026) · doi:10.36227/techrxiv.175329122.29020847/v3
An Integration of Blockchain and Machine Learning into the Health Care System: A Meta-Analysis Approach
Abdelraouf, M., & Mohamed, I. (2026) · Journal of Hospital Librarianship · doi:10.1080/15323269.2025.2603437
BMchain: Design of an Account-Driven and Regulatory-Friendly Stateless Layer-2 Blockchain
Xue, L., Wang, M., & Wang, X. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3656818

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Prashanth, K., & Kumar, D. (2025) · International Journal of Engineering Research and Science & Technology · doi:10.62643/ijerst.2025.v21.i2.pp1032-1042
Federated learning on the blockchain with SMPC model verification for healthcare systems protected from poisoning attacks
Kasturi, A., Pooja, A., Sharanya, B., Deena, C., & Keerthana, D. (2024) · International Journal of Cloud Computing and Database Management · doi:10.33545/27075907.2024.v5.i2a.67
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Reddy, M. L. N., & Hannah, J. (2026) · International Journal of Science and Research (IJSR) · doi:10.21275/sr26302215304
Blockchain-Powered Secure Federated Learning for Healthcare: Privacy-Guaranteed AI Training with ZKP-Enhanced SMPC and Tamper-Proof Model Aggregation
Nishitha, C., Yelavarti, K. C., & Jyothi, V. E. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06694-7_21
Trustworthy Federated Learning with Blockchain-Based Consensus for Mitigating Poisoning Attacks in Healthcare Systems
Alhamrani, R. H., Bamashmoos, F., & Khairallah, E. F. (2026) · Information · doi:10.3390/info17020201 · Cited by 5 · see citing works

Authors: Kalapaaking, A. P., Khalil, I., & Yi, X.
Affiliations: RMIT University (AU)
ORCID: Kalapaaking, Aditya Pribadi
Khalil, Ibrahim
Yi, Xun
Venue: IEEE Transactions on Emerging Topics in Computing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024-01
Volume: 12
Issue: 1
Pages: 269-280
ISSN: 2168-6750 (ISSN-L)
2376-4562
DOI: 10.1109/tetc.2023.3268186
OpenAlex: W4366678696
Cited by: 122
References: 30
Citations by year: 2026: 34 · 2025: 45 · 2024: 28 · 2023: 15
FWCI: 11.49
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Emerging Topics in Computing (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Australian Research Council (DP210102761)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords: blockchain-based federated learning, secure multi-party computation, model verification, poisoning attacks, health care system, encrypted inference, privacy-preserving machine learning

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Blockchain-Based Federated Learning With SMPC Model Verification Against Poisoning Attack for Healthcare Systems

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