Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
Federated learning for edge computing: A survey
New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …
network, allowing edge devices to train simple models that can then be deployed in practice …
Data heterogeneity-robust federated learning via group client selection in industrial IoT
Nowadays, the Industrial Internet of Things (IIoT) has played an integral role in Industry 4.0
and produced massive amounts of data for industrial intelligence. These data locate on …
and produced massive amounts of data for industrial intelligence. These data locate on …
Blockchain-based two-stage federated learning with non-IID data in IoMT system
The Internet of Medical Things (IoMT) has a bright future with the development of smart
mobile devices. Information technology is also leading changes in the healthcare industry …
mobile devices. Information technology is also leading changes in the healthcare industry …
DEEP-FEL: Decentralized, efficient and privacy-enhanced federated edge learning for healthcare cyber physical systems
The rapid development of Internet of Things (IoT) stimulates the innovation for the health-
related devices such as remote patient monitoring, connected inhalers and ingestible …
related devices such as remote patient monitoring, connected inhalers and ingestible …
Deep learning in diverse intelligent sensor based systems
Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …
virtually all fields of science and engineering. The increasing complexity and the large …
FLAD: adaptive federated learning for DDoS attack detection
R Doriguzzi-Corin, D Siracusa - Computers & Security, 2024 - Elsevier
Federated Learning (FL) has been recently receiving increasing consideration from the
cybersecurity community as a way to collaboratively train deep learning models with …
cybersecurity community as a way to collaboratively train deep learning models with …
FLY-SMOTE: Re-balancing the non-IID iot edge devices data in federated learning system
R Younis, M Fisichella - IEEE Access, 2022 - ieeexplore.ieee.org
In recent years, the data available from IoT devices have increased rapidly. Using a machine
learning solution to detect faults in these devices requires the release of device data to a …
learning solution to detect faults in these devices requires the release of device data to a …
Decentralized federated learning for internet of things anomaly detection
With the improvement of computing power and the development of network technology,
Internet of Things (IoT) devices are widely used in many industries. But it also faces various …
Internet of Things (IoT) devices are widely used in many industries. But it also faces various …
Federated learning with gan-based data synthesis for non-iid clients
Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative
learning paradigm. However, it suffers from the non-independent and identically distributed …
learning paradigm. However, it suffers from the non-independent and identically distributed …