[AISWorld] cfp Special Issue "Federated Learning for Blockchain Assisted IoT Systems: Architecture, Algorithms, And Applications", Connection Science

[Adhiyaman M]

Dear Editors,

                      Many thanks for posting our call for paper on the
journal website. We will make our SI a successful one. Once the submission
system is ready, kindly share the guest editor login credentials. Look
forward to working with you.



Regards,



*Dr. Adhiyaman Manickam*

*Research Scientist,*

*Perception, Robotics and Intelligent Machines (PRIME), *

*Department of Computer Science,*

*University of Moncton, Canada*

*E: **adhiyaman.manickam at umoncton.ca* <adhiyaman.manickam at umoncton.ca%20%20>

*GS: **https://scholar.google.co.in/citations?user=HNO0DXIAAAAJ&hl=en*
<https://scholar.google.co.in/citations?user=HNO0DXIAAAAJ&hl=en>

On Sun, Jun 12, 2022 at 7:21 AM Editorial Manager <
journaleditorialmanager at outlook.com> wrote:

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> [Apologies if multiple copies of this email are received]
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> Special issue: Federated Learning for Blockchain Assisted IoT Systems:
> Architecture, Algorithms, And Applications
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> Connection Science, Taylor & Francis
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> https://tinyurl.com/2waw8pe8
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> CiteScore: 3.4 (2021)
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> The FL system for blockchain assisted IoT systems to work in two important
> phases. In the first phase, the raw model provided by the manufacturers
> undergoes training based on the data collected in the user’s environment.
> In certain situations, the data can be collected through pervasive devices
> such as smartphones, laptops etc., and processed through mobile edge
> computing services. These data models are then authorized by the users and
> sent to the blockchain systems. The centralized aggregator in the FL
> systems is replaced by the blockchain system in order to eliminate
> malicious or tampered data. In the second phase, an average of the received
> data models is calculated or mined. In the whole process, the user’s
> organization will act as a mining system hence helping in crowdsourcing.
> Research findings also suggest a normalization technique to further secure
> the privacy of user data. The applications of FL have extended to a great
> extent in the Industrial Internet of Things (IIoT) since it has an
> emergency privacy protection ML algorithm. Nevertheless, the efficacy of FL
> systems for blockchain-assisted IoT suffers from certain drawbacks such as
> difficulty in handling heterogeneous data, scalability etc. Research must
> be focused on improving the drawbacks and enhancing the overall performance
> and application range of such systems.
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> Original contributions are being sought in a wide range of related topics
> including, but not limited to, the following:
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> - Federated Learning (FL) for blockchain-powered edge intelligence for
> secure data transfer.
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> - Techniques to address the existing limitations in blockchain-based edge
> intelligence using federated learning systems.
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> - Framework for federated learning in blockchain-assisted Industrial
> Internet of Things (IIoT).
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> - Novel system architecture for healthcare database management using IoT
> and FL.
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> - Methods to overcome scalability issues in FL based IoT secured by
> blockchain technologies.
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> - Comparison of federated learning and classical distributed learning in
> terms of performance and efficiency for smart home systems.
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> - Innovative solutions for addressing system heterogeneity in pervasive
> mobile devices using FL and blockchain technologies.
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> - Federated optimization in blockchain-based heterogeneous smart and
> intelligent healthcare database networks.
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> - Development of an optimum resource allocation algorithm for FL in
> blockchain assisted IoT applications.
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> - Integration of asynchronous communication approach for federated
> learning in smart transportation systems.
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> Important Date
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> Manuscript deadline:  10 October 2022
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> Guest Editor(s)
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> Adhiyaman Manickam, Department of Computer Science University of Moncton,
> Canada
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> adhiyaman.m at ieee.org
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> J. Alfred Daniel, Dhanalakshmi Srinivasan Engineering College, Anna
> University, India.
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> alfreddaniel.j at ieee.org
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> Dinesh Jackson Samuel, The University of Texas MD Anderson Cancer Center,
> Texas, United States
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> rsamuel at ieee.org
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> Inquiries: for any questions about this Special issue, please contact the
> guest editors.
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