A survey on IoT-edge-cloud continuum systems: status, challenges, use cases, and open issues
The rapid growth in the number of interconnected devices on the Internet (referred to as the
Internet of Things—IoT), along with the huge volume of data that are exchanged and …
Internet of Things—IoT), along with the huge volume of data that are exchanged and …
Sailing into the future: technologies, challenges, and opportunities for maritime communication networks in the 6G era
The maritime domain is a major driver of economic growth with emerging services,
comprising intelligent transportation systems (ITSs), smart ports, security and safety, and …
comprising intelligent transportation systems (ITSs), smart ports, security and safety, and …
[HTML][HTML] Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
Federated Learning-Aided Prognostics in the Ship** 4.0: Principles, Workflow, and Use Cases
The next generation of ship** industry, namely Ship** 4.0 will integrate advanced
automation and digitization technologies towards revolutionizing the maritime industry. As …
automation and digitization technologies towards revolutionizing the maritime industry. As …
Empowering 6G maritime communications with distributed intelligence and over-the-air model sharing
Introduction: Ship** and maritime transportation have gradually gained a key role in
worldwide economical strategies and modern business models. The realization of Smart …
worldwide economical strategies and modern business models. The realization of Smart …
FeL-MAR: Federated learning based multi resident activity recognition in IoT enabled smart homes
This study explores and proposes the use of a Federated Learning (FL) based approach for
recognizing multi-resident activities in smart homes utilizing a diverse array of data collected …
recognizing multi-resident activities in smart homes utilizing a diverse array of data collected …
[HTML][HTML] Federated Learning: Navigating the Landscape of Collaborative Intelligence
As data become increasingly abundant and diverse, their potential to fuel machine learning
models is increasingly vast. However, traditional centralized learning approaches, which …
models is increasingly vast. However, traditional centralized learning approaches, which …
[HTML][HTML] Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting
This study examines the impact of data integrity attacks on Federated Learning (FL) for load
forecasting in smart grid systems, where privacy-sensitive data require robust management …
forecasting in smart grid systems, where privacy-sensitive data require robust management …
Personalized Federated Learning Incorporating Adaptive Model Pruning at the Edge
Y Zhou, G Duan, T Qiu, L Zhang, L Tian, X Zheng… - Electronics, 2024 - mdpi.com
Edge devices employing federated learning encounter several obstacles, including (1) the
non-independent and identically distributed (Non-IID) nature of client data,(2) limitations due …
non-independent and identically distributed (Non-IID) nature of client data,(2) limitations due …
Deep Reinforcement Learning for Smart Home Temperature Comfort in IoT-Edge Computing Systems
M Christopoulos, S Spantideas… - Proceedings of the 1st …, 2024 - dl.acm.org
In this paper, a novel IoT-Edge-Cloud (IEC) computing system designed for multiple Smart
Homes is introduced, with a focus on supporting Home Energy Management Systems …
Homes is introduced, with a focus on supporting Home Energy Management Systems …