Federated learning for connected and automated vehicles: A survey of existing approaches and challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
Federated learning-empowered mobile network management for 5G and beyond networks: From access to core
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …
integrated communication and computing platform that will enable multipurpose and …
Federated learning in vehicular networks
Machine learning (ML) has recently been adopted in vehicular networks for applications
such as autonomous driving, road safety prediction and vehicular object detection, due to its …
such as autonomous driving, road safety prediction and vehicular object detection, due to its …
A survey of federated learning for connected and automated vehicles
Connected and Automated Vehicles (CAVs) represent a rapidly growing technology in the
automotive domain sector, offering promising solutions to address challenges such as traffic …
automotive domain sector, offering promising solutions to address challenges such as traffic …
Terahertz-band channel and beam split estimation via array perturbation model
For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz
(THz)-band has been envisioned as one of the key enabling technologies for the sixth …
(THz)-band has been envisioned as one of the key enabling technologies for the sixth …
[HTML][HTML] An improved big data analytics architecture using federated learning for IoT-enabled urban intelligent transportation systems
The exponential growth of the Internet of Things has precipitated a revolution in Intelligent
Transportation Systems, notably in urban environments. An ITS leverages advancements in …
Transportation Systems, notably in urban environments. An ITS leverages advancements in …
Semi-federated learning: Convergence analysis and optimization of a hybrid learning framework
Under the organization of the base station (BS), wireless federated learning (FL) enables
collaborative model training among multiple devices. However, the BS is merely responsible …
collaborative model training among multiple devices. However, the BS is merely responsible …
Federated Semantic Learning Driven by Information Bottleneck for Task-Oriented Communications
In this letter, we present a novel federated semantic learning (FedSem) framework to
collaboratively train the semantic-channel encoders of multiple devices with the coordination …
collaboratively train the semantic-channel encoders of multiple devices with the coordination …
A d2d-aided federated learning scheme with incentive mechanism in 6G networks
R Fantacci, B Picano - IEEE Access, 2022 - ieeexplore.ieee.org
Pervasive new era applications are expected to involve massive amount of data to
implement intelligent distributed frameworks based on machine learning, supported by sixth …
implement intelligent distributed frameworks based on machine learning, supported by sixth …
Semi-federated learning for connected intelligence with computing-heterogeneous devices
J Han, W Ni, L Li - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Federated learning (FL) is a promising distributed learning approach which enables multiple
devices to collaboratively train deep neural networks in a privacy-preserving fashion …
devices to collaboratively train deep neural networks in a privacy-preserving fashion …