When decentralized optimization meets federated learning
Federated learning is a new learning paradigm for extracting knowledge from distributed
data. Due to its favorable properties in preserving privacy and saving communication costs …
data. Due to its favorable properties in preserving privacy and saving communication costs …
Joint optimization of video-based AI inference tasks in MEC-assisted augmented reality systems
The high computational complexity and energy consumption of artificial intelligence (AI)
algorithms hinder their application in augmented reality (AR) systems. However, mobile …
algorithms hinder their application in augmented reality (AR) systems. However, mobile …
Collaborative authentication for 6G networks: An edge intelligence based autonomous approach
The conventional device authentication of wireless networks usually relies on a security
server and centralized process, leading to long latency and risk of single-point of failure …
server and centralized process, leading to long latency and risk of single-point of failure …
Multiple parallel federated learning via over-the-air computation
This paper investigates multiple parallel federated learning in cellular networks, where a
base station schedules several FL tasks in parallel and each task has a group of devices …
base station schedules several FL tasks in parallel and each task has a group of devices …
Performance-oriented design for intelligent reflecting surface assisted federated learning
To efficiently exploit the massive amounts of raw data that are increasingly being generated
in mobile edge networks, federated learning (FL) has emerged as a promising distributed …
in mobile edge networks, federated learning (FL) has emerged as a promising distributed …
Network for Distributed Intelligence: A Survey and Future Perspectives
To keep pace with the explosive growth of Artificial Intelligence (AI) and Machine Learning
(ML)-dominated applications, distributed intelligence solutions are gaining momentum …
(ML)-dominated applications, distributed intelligence solutions are gaining momentum …
Semantic communications for image recovery and classification via deep joint source and channel coding
With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G)
networks need to support new AI tasks such as classification and clustering apart from data …
networks need to support new AI tasks such as classification and clustering apart from data …
Performance optimization for variable bitwidth federated learning in wireless networks
This paper considers improving wireless communication and computation efficiency in
federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge …
federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge …
Edge learning for large-scale Internet of Things with task-oriented efficient communication
In Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent
applications and services. As the network size becomes large, different users may generate …
applications and services. As the network size becomes large, different users may generate …
Exploiting UAV for air-ground integrated federated learning: A joint UAV location and resource optimization approach
Y Jing, Y Qu, C Dong, W Ren, Y Shen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Recently, many exciting usage scenarios and groundbreaking technologies for sixth
generation (6G) networks have drawn more and more attention. The revolution of 6G mainly …
generation (6G) networks have drawn more and more attention. The revolution of 6G mainly …