A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology

Saurabh Singh, Shailendra Rathore, Osama Alfarraj, Amr Tolba, Byungun Yoon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 languageEnglish
Pages (from-to)380-388
Number of pages9
JournalFuture Generation Computer Systems
Early online date12 Dec 2021
Publication statusPublished - 1 Apr 2022


  • Federated Learning
  • Privacy-preserving
  • Blockchain
  • Internet-of-Things

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