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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.3
- 발행연도
- 2024.6
- 수록면
- 254 - 262 (9page)
- DOI
- 10.5573/IEIESPC.2024.13.3.254
이용수
초록· 키워드
오류제보하기
In recent trends of growth in technologies, data management, maintenance of medical records, sharing of data, diagnosis of disease, and medication are the key areas where digital healthcare plays a vital role. Despite enormous improvement, handling huge amounts of data, privacy, secure sharing, accuracy, and computational speed remains challenging. Federated learning is a machine learning technology that allows distributed model training using users’ own data to train a model. The model update is done through a central server that aggregates individual users and sends a global model. This ensures privacy protection and is suitable for handling large data. Blockchain technology is a publicly distributed ledger that collects the information of nodes as blocks and sends a copy to all nodes in the network so that data transparency is maintained and secure. However, blockchain has a limitation in handling large volumes of data. In such cases, federated learning can be used with a blockchain for better performance. By integrating federated learning with blockchain, accurate prediction, computational speed, data security, privacy, and accuracy can be achieved. A comprehensive review of how various federated learning technologies can integrate with blockchain networks to achieve accuracy and efficiency is presented.
목차
Abstract
1. Introduction
2. Related Work
3. Healthcare using Federated Learning and Blockchain
5. Conclusion
References
참고문헌 (20)
참고문헌 신청
Fozoonmayeh, Donya, Hai Vu Le, Ekaterina Wittfoth, Chong Geng, Natalie Ha, Jingjue Wang, Maria Vasilenko, Yewon Ahn, and Diane Myung-kyung Woodbridge. "A scalable smartwatch-based medication intake detection system using distributed machine learning." Journal of medical systems 44 (2020): 1-14 Article (CrossRef Link).
Chen, Yiqiang, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao. "Fedhealth: A federated transfer learning framework for wearable healthcare." IEEE Intelligent Systems 35, no. 4 (2020): 83-93 Article (CrossRef Link).
Ali, Abdelmgeid A. "Stroke prediction using distributed machine learning based on Apache spark." Stroke 28, no. 15 (2019): 89-97 Article (CrossRef Link).
Qayyum, Adnan, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, and Junaid Qadir. "Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge." IEEE Open Journal of the Computer Society 3 (2022): 172-184 Article (CrossRef Link).
Annamalai, Chinnappa, Chellavelu Vijayakumaran, Vijayakumar Ponnusamy, and Hyunsung Kim. "Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment." Sensors 23, no. 12 (2023): 5596 Article (CrossRef Link).
Chen, Yiqiang, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao. "Fedhealth: A federated transfer learning framework for wearable healthcare." IEEE Intelligent Systems 35, no. 4 (2020): 83-93 Article (CrossRef Link).
Ali, Abdelmgeid A. "Stroke prediction using distributed machine learning based on Apache spark." Stroke 28, no. 15 (2019): 89-97 Article (CrossRef Link).
Qayyum, Adnan, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, and Junaid Qadir. "Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge." IEEE Open Journal of the Computer Society 3 (2022): 172-184 Article (CrossRef Link).
Annamalai, Chinnappa, Chellavelu Vijayakumaran, Vijayakumar Ponnusamy, and Hyunsung Kim. "Optimal ElGamal Encryption with Hybrid Deep-Learning-Based Classification on Secure Internet of Things Environment." Sensors 23, no. 12 (2023): 5596 Article (CrossRef Link).
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