Review and analysis of recent advances in intelligent network softwarization for the Internet of Things
MA Zormati, H Lakhlef, S Ouni - Computer Networks, 2024 - Elsevier
Abstract The Internet of Things (IoT) is an emerging technology that aims to connect
heterogeneous and constrained objects to each other and to the Internet. It has grown …
heterogeneous and constrained objects to each other and to the Internet. It has grown …
Federated learning for 5G base station traffic forecasting
Cellular traffic prediction is of great importance on the path of enabling 5G mobile networks
to perform intelligent and efficient infrastructure planning and management. However …
to perform intelligent and efficient infrastructure planning and management. However …
Towards energy-aware federated traffic prediction for cellular networks
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G)
networks and beyond, as accurate forecasting is essential for intelligent network design …
networks and beyond, as accurate forecasting is essential for intelligent network design …
An Overview of Machine Learning-Enabled Network Softwarization for the Internet of Things
MA Zormati, H Lakhlef - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
The Internet of Things (IoT) has evolved from a novel technology to an integral part of our
everyday lives. It encompasses a multitude of heterogeneous devices that collect valuable …
everyday lives. It encompasses a multitude of heterogeneous devices that collect valuable …
Federated learning in mobile networks: A comprehensive case study on traffic forecasting
The increasing demand for efficient resource allocation in mobile networks has catalyzed
the exploration of innovative solutions that could enhance the task of real-time cellular traffic …
the exploration of innovative solutions that could enhance the task of real-time cellular traffic …
A dynamic receptive field and improved feature fusion approach for federated learning in financial credit risk assessment
R Li, Y Cao, Y Shu, J Guo, B Shi, J Yu, Y Di, Q Zuo… - Scientific Reports, 2024 - nature.com
Federated Learning (FL) uses local data to perform distributed training on clients and
combines resulting models on a public server to mitigate privacy exposure by avoiding data …
combines resulting models on a public server to mitigate privacy exposure by avoiding data …
The Future of Large Language Model Pre-training is Federated
Generative pre-trained large language models (LLMs) have demonstrated impressive
performance over a wide range of tasks, thanks to the unprecedented amount of data they …
performance over a wide range of tasks, thanks to the unprecedented amount of data they …
The implications of decentralization in blockchained federated learning: Evaluating the impact of model staleness and inconsistencies
Blockchain promises to enhance distributed machine learning (ML) approaches such as
federated learning (FL) by providing further decentralization, security, immutability, and trust …
federated learning (FL) by providing further decentralization, security, immutability, and trust …
Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours
For widespread real-world applications, it is beneficial for robots to explore Federated
Learning (FL) settings where several robots, deployed in parallel, can learn independently …
Learning (FL) settings where several robots, deployed in parallel, can learn independently …
A Declarative Query Language for Scientific Machine Learning
HM Jamil - arXiv preprint arXiv:2405.16159, 2024 - arxiv.org
The popularity of data science as a discipline and its importance in the emerging economy
and industrial progress dictate that machine learning be democratized for the masses. This …
and industrial progress dictate that machine learning be democratized for the masses. This …