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Advanced Blockchain and Federated Learning Techniques Towards Secure Cloud Computing

Edited by:
Professor Yuan Liu, PhD: Cyberspace Institute of Advanced Technology, Guangzhou University, China
Professor Jie Zhang, PhD: School of Computer Science and Engineering, Nanyang Technological University, Singapore
Assistant Professor Athirai A. Irissappane, PhD: School of Engineering and Technology, University of Washington, USA
Honorary Lecturer Zhu Sun, PhD: School of Computing, Macquarie University, Australia

Submission Status: Open   |   Submission Deadline: 30 April 2024 


Journal of Cloud Computing is calling for submissions to our Collection on 'Advanced Blockchain and Federated Learning Techniques Towards Secure Cloud Computing.' This Collection aims to present state-of-the-art, research challenges, solutions, and applications of advanced blockchain and FL techniques targeting at building a secure cloud computing environment.

About the collection

Due to the distributed nature of cloud computing systems, many challenging attacks or threats happen, such as distributed denial of service (DDoS) attack. The conventionally centralized anomaly detection mechanisms are not feasible due to the reluctance of security related data sharing and lack of effective incentive motivations. Meanwhile, Federated learning (FL) and blockchain are two new computing paradigms in the recent decade, which are fast developing and brings innovative solutions to protect the security of various application systems, such as threat detection, secure computation, attack prevention, and so on. FL can enable an artificial intelligent model to be trained by isolated private datasets without collecting them together, thus the privacy of the private data is preserved. Blockchain systems can be treated as distributed ledgers to record and share security data in a traceable manner, contributing the infrastructural foundation for practical information technology systems. Combining the both can even expand the emerging capabilities and is potential to reshape the current landscape of security techniques.

This Collection aims to present state-of-the-art, research challenges, solutions, and applications of advanced blockchain and FL techniques targeting at building a secure cloud computing environment. It also aims to cover various aspects of blockchain and FL based framework that supports cyber security. The outcome will be a collection of articles that propose advanced techniques for in the domain of security of cloud computing, blockchain and FL.

Topics of interest (not limited to):

  • Security architecture design of secure cloud computing based on blockchain or FL
  • New attack or threat revelation mechanism for cloud computing based on blockchain or FL
  • Attack mitigation mechanisms for cloud computing based on blockchain or FL
  • Privacy-preserved federated learning for blockchain empowered cyber security
  • Efficient incentive mechanism for sharing threat information based on Blockchain
  • Secure multiple computation for cyber security with blockchain or FL
  • DDos attacks mitigation mechanisms for cloud computing based on blockchain or FL

  2. Because of its excellent properties of fault tolerance, efficiency and availability, the practical Byzantine fault tolerance (PBFT) algorithm has become the mainstream consensus algorithm in blockchain. Howeve...

    Authors: Juan Liu, Xiaohong Deng, Wangchun Li and Kangting Li
    Citation: Journal of Cloud Computing 2024 13:74
    Content type: Research Published on:

  3. Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data. Federated learning provides a method to protect p...

    Authors: Huiyong Wang, Qi Wang, Yong Ding, Shijie Tang and Yujue Wang
    Citation: Journal of Cloud Computing 2024 13:62
    Content type: Research Published on:

  4. With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT c...

    Authors: Kai Yang, Jiawei Du, Jingchao Liu, Feng Xu, Ye Tang, Ming Liu and Zhibin Li
    Citation: Journal of Cloud Computing 2024 13:57
    Content type: Research Published on:

    The Correction to this article has been published in Journal of Cloud Computing 2024 13:75

  5. Web3.0 represents the ongoing evolution of blockchain technology, placing a strong emphasis on establishing a decentralized and user-controlled Internet. Current data delegation solutions for Web3.0 predominan...

    Authors: Hongmin Gao, Pengfei Duan, Xiaofeng Pan, Xiaojing Zhang, Keke Ye and Ziyuan Zhong
    Citation: Journal of Cloud Computing 2024 13:21
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of Research Articles. 

Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection, please select "Advanced Blockchain and Federated Learning Techniques Towards Secure Cloud Computing" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.