ArticleComputational intelligence and neuroscience2021
A Blockchain-Based Federated Learning Method for Smart Healthcare.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Ensuring the integrity of AI models: a blockchain-based approach for protecting medical imaging training data.Scientific reports · 2026Article
- Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework.JMIR diabetes · 2026Article
- Robust federated learning for cloud environments using evolutionary optimization and blockchain.Scientific reports · 2026Article
- Blockchain Technology to Enhance Clinical Data Management in Healthcare: A Systematic Literature Review.Blockchain in healthcare today · 2026Review
- IoT Authentication in Federated Learning: Methods, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2025Article
- Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management.Scientific reports · 2025Article
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.Patterns (New York, N.Y.) · 2024Review
- Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions.Cell reports. Medicine · 2024Review
- A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain.PeerJ. Computer science · 2024Article
- A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems.International journal of environmental research and public health · 2023Review
- Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.Cluster computing · 2022Article
- Combination of Blockchain and AI for Music Intellectual Property Protection.Computational intelligence and neuroscience · 2022Article
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.