Vehicle as a service (VaaS): Leverage vehicles to build service networks and capabilities for smart cities
Smart cities demand resources for rich immersive sensing, ubiquitous communications,
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …
powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of …
Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
V2X cooperative perception for autonomous driving: Recent advances and challenges
Accurate perception is essential for advancing autonomous driving and addressing safety
challenges in modern transportation systems. Despite significant advancements in computer …
challenges in modern transportation systems. Despite significant advancements in computer …
Fedmfs: Federated multimodal fusion learning with selective modality communication
Federated learning (FL) is a distributed machine learning (ML) paradigm that enables clients
to collaborate without accessing, infringing upon, or leaking original user data by sharing …
to collaborate without accessing, infringing upon, or leaking original user data by sharing …
Communication-efficient multimodal federated learning: Joint modality and client selection
Multimodal federated learning (FL) aims to enrich model training in FL settings where clients
are collecting measurements across multiple modalities. However, key challenges to …
are collecting measurements across multiple modalities. However, key challenges to …
Digital ethics in federated learning
The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing
concern over the protection of data privacy and the limitation of data misuse. Federated …
concern over the protection of data privacy and the limitation of data misuse. Federated …
Decentralized federated learning: Model update tracking under imperfect information sharing
VP Chellapandi, A Upadhyay, A Hashemi… - arXiv preprint arXiv …, 2024 - arxiv.org
A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm
(FedNMUT) is proposed, which is tailored to function efficiently in the presence of noisy …
(FedNMUT) is proposed, which is tailored to function efficiently in the presence of noisy …
Advance-FL: A3C-based Adaptive Asynchronous Online Federated Learning for Vehicular Edge Cloud Computing Networks
Enhancing autonomous driving through Federated Learning (FL) in Intelligent Connected
Vehicles (ICVs) confronts challenges like limited scalability of central management …
Vehicles (ICVs) confronts challenges like limited scalability of central management …
Federated Object Detection Scenarios for Intelligent Vehicles: Review, Case Studies, Experiments and Discussions
The performance of intelligent vehicles (IVs) in object detection relies not only on the design
or scale of the CNN model they use but also on how effectively they share their acquired …
or scale of the CNN model they use but also on how effectively they share their acquired …
A Framework of Decentralized Federated Learning With Soft Clustering and 1-Bit Compressed Sensing for Vehicular Networks
Federated Learning (FL) has been recognized as a transformative approach in vehicular
networks, enabling collaborative training between vehicles and preserving data privacy …
networks, enabling collaborative training between vehicles and preserving data privacy …