Advances in
Federated Learning for Blockchain Systems and
Industrial IoT
In recent years, mobile technology and the internet of objects have been used in mobile networks to meet new technical demands. The advancement of the technology focuses on data communication, the reduction of transmission and network load. The emerging needs have centered on data storage, computation and low latency management in potentially smart city, transport, smart grids and a wide number of sustainable environments. Federated Leaning's contributions include an effective framework to improve network security in heterogeneous Industrial Internet of Things (IIoT) environments. Federated Learning (FL) is a bottle neck technology that improves wireless paradigm privacy and security problems. Federated Learning is a platform which promotes the connectivity of intelligent systems with increased network capacity, service quality, accessibility of the network and user experience. Blockchain is a technology which is exposed and can contribute to stability in IIoT. Blockchain appears to be a mechanism to preserve IIoT and retain confidentiality of user / data, and the capacity to provide unauthorized reproductive and information services.
Topics of Interest -
Researchers, developers, and industry experts are welcome to contribute to one of the followings topics or slightly similar ones:
· FL in vehicular networks
· Integration of Blockchain and FL in Beyond 5G/6G Network Architectures
· FL for large-scale Internet of Things
· Blockchain with lightweight computation
· Blockchain-based service and applications for vehicular clouds
· FL for future internet architectures
· Scalable Blockchain for intelligent networking services
· Application of FL in large-scale intelligent networking services
· Blockchain for emerging networks
· Byzantine-tolerant FL
· Churn-tolerant FL
· FL for NGN and 6G
· FL for IoT healthcare systems
· Federated based traffic offloading prediction and optimization
· Spectrum Sensing, Spectrum Management
· Blockchain and IoT applications
· Artificial intelligence approaches for unmanned aerial vehicles (UAVs)
· Edge/IoT based wireless communication
· Deep learning for edge computing networks
· Training scheme of Federated Learning model
· Segmentation of Federated Learning models for collaborative intelligence between cloud and the edge
Submission Schedule –
The Issue is open to receive papers till 30thJune 2022 and papers will be published on continues basis as per the acceptance cycle.
Lead Guest Editors –
1. Dr. Sandeep Kautish,
Professor and Dean-Academics,
LBEF Campus, Kathmandu Nepal (Asia Pacific University of Technology &
Innovation, Malaysia)
Email – sandeep.kautish@lbef.edu.np
Google Scholar - https://scholar.google.co.in/citations?user=O3mUpVQAAAAJ&hl=en
2. Dr. Gaurav Dhiman,
Govt. Bikram College of Commerce, India,
Email - gdhiman0001@gmail.com
Google Scholar - https://scholar.google.co.in/citations?hl=en&user=E3Z7oJcAAAAJ