Harnessing federated generative learning for green and sustainable Internet of Things

The high integration of FL to achieve data privacy preservation in modern mobile applications is bringing users' data to become the most valuable assets in the challenge of realizing robust data privacy policies inside network infrastructures. This means, in other words, that today, securing sensitive user data is becoming more essential than before. In this article, by using FL based privacy preserving ecosystem learn from the situation of data centralized context, we adopt the differential data sharing to realize high performance inside FL blockchain by increasing also it preservation performance. Our result performances by comparing privacy and non-privacy preserving chaincode FL Blockchain shows high performance on keeping protected the data shared environment. Hyperledger Fabric algorithm design simulation scenario reduces considerably all addition noise to keep the entire system work properly. This contribution shows how mobile communication network systems in data privacy and preservation context can be incentivized to allow any users through interchange connection to be rewarded for their work performances, and how differential data sharing scenario can useful to deal with it.

show abstract

Energy-Efficient Industrial Internet of Things in Green 6G Networks

Fernando,

Lăzăroiu

2024

Applied Sciences

View full text Add to dashboard Cite

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

show abstract

Harnessing federated generative learning for green and sustainable Internet of Things

Cited by 4 publications

References 34 publications

Privacy-preserving federated learning for proactive maintenance of IoT-empowered multi-location smart city facilities

Privacy-preserving federated learning for proactive maintenance of IoT-empowered multi-location smart city facilities

Research implementation of FL privacy-preserving blockchain and differential data sharing

Energy-Efficient Industrial Internet of Things in Green 6G Networks

Contact Info

Product

Resources

About