AI-empowered fog/edge resource management for IoT applications: A comprehensive review, research challenges and future perspectives
The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several
domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to …
domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to …
[HTML][HTML] 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 …
Resource constrained vehicular edge federated learning with highly mobile connected vehicles
This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge
server leverages highly mobile connected vehicles'(CVs') onboard central processing units …
server leverages highly mobile connected vehicles'(CVs') onboard central processing units …
Fedict: Federated multi-task distillation for multi-access edge computing
The growing interest in intelligent services and privacy protection for mobile devices has
given rise to the widespread application of federated learning in Multi-access Edge …
given rise to the widespread application of federated learning in Multi-access Edge …
[HTML][HTML] Resource-aware multi-task offloading and dependency-aware scheduling for integrated edge-enabled IoV
Abstract Internet of Vehicles (IoV) enables a wealth of modern vehicular applications, such
as pedestrian detection, real-time video analytics, etc., that can help to improve traffic …
as pedestrian detection, real-time video analytics, etc., that can help to improve traffic …
[HTML][HTML] A state-of-the-art review of task scheduling for edge computing: A delay-sensitive application perspective
The edge computing paradigm enables mobile devices with limited memory and processing
power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications …
power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications …
Dynamic Data Sample Selection and Scheduling in Edge Federated Learning
Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …
enables distributed learning to train on cross-device data, achieving efficient performance …
Federated Learning-Based Misbehaviour Detection for the 5G-Enabled Internet of Vehicles
The concept of federated learning (FL) is becoming increasingly popular as a method for
training collaborative models without loss the sensitive information. The term has become …
training collaborative models without loss the sensitive information. The term has become …
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 …
[HTML][HTML] Internet of Vehicles and real-time optimization algorithms: Concepts for vehicle networking in smart cities
Achieving sustainable freight transport and citizens' mobility operations in modern cities are
becoming critical issues for many governments. By analyzing big data streams generated …
becoming critical issues for many governments. By analyzing big data streams generated …