Abstract
With the dramatically increasing deployment of IoT (Internet-of-Things) and communication, data has always been a major priority to achieve intelligent healthcare in a smart city. For the modern environment, valuable assets are user IoT data. The privacy policy is even the biggest necessity to secure user's data in a deep-rooted fundamental infrastructure of network and advanced applications, including smart healthcare. Federated learning acts as a special machine learning technique for privacy-preserving and offers to contextualize data in a smart city. This article proposes Blockchain and Federated Learning-enabled Secure Architecture for Privacy-Preserving in Smart Healthcare, where Blockchain-based IoT cloud platforms are used for security and privacy. Federated Learning technology is adopted for scalable machine learning applications like healthcare. Furthermore, users can obtain a well-trained machine learning model without sending personal data to the cloud. Moreover, it also discussed the applications of federated learning for a distributed secure environment in a smart city.
Original language | English |
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Pages (from-to) | 380-388 |
Number of pages | 9 |
Journal | Future Generation Computer Systems |
Volume | 129 |
DOIs | |
State | Published - Apr 2022 |
Keywords
- Blockchain
- Federated Learning
- Internet-of-Things
- Privacy-preserving