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Found 16 papers, 1 papers with code
Private Knowledge Sharing in Distributed Learning: A Survey
In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities.
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing
To tackle this problem, Split Federated Learning is utilized, where clients upload their intermediate model training outcomes to a cloud server for collaborative server-client model training.
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges
To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies.
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT).
Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN).
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing
To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs.
Federated Learning for Smart Healthcare: A Survey
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing
COVID-19 has spread rapidly across the globe and become a deadly pandemic.
Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning
The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.
BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain
The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations.
6G Internet of Things: A Comprehensive Survey
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.
Federated Learning for Industrial Internet of Things in Future Industries
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.
Federated Learning for Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).
Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications
Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks.
Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning
How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem.
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications.
