|
|
|
|
|
Hanyue Xu, Kah Phooi Seng, Jeremy Smith and Li Minn Ang
In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the co...
ver más
|
|
|
|
|
|
|
|
|
|
|
Feng Zhou, Shijing Hu, Xin Du, Xiaoli Wan and Jie Wu
In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network band...
ver más
|
|
|
|
|
|
|
|
|
|
|
Changhao Wu, Siyang He, Zengshan Yin and Chongbin Guo
Large-scale low Earth orbit (LEO) remote satellite constellations have become a brand new, massive source of space data. Federated learning (FL) is considered a promising distributed machine learning technology that can communicate optimally using these ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tuan Phong Tran, Anh Hung Ngoc Tran, Thuan Minh Nguyen and Myungsik Yoo
Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited nu...
ver más
|
|
|
|
|
|
|
|
|
|
|
David Naseh, Mahdi Abdollahpour and Daniele Tarchi
This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed in...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wenbo Zhang, Yuchen Zhao, Fangjing Li and Hongbo Zhu
Federated learning is currently a popular distributed machine learning solution that often experiences cumbersome communication processes and challenging model convergence in practical edge deployments due to the training nature of its model information ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Fotis Nikolaidis, Moysis Symeonides and Demetris Trihinas
Federated learning (FL) is a transformative approach to Machine Learning that enables the training of a shared model without transferring private data to a central location. This decentralized training paradigm has found particular applicability in edge ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Weihong Cai and Fengxi Duan
With the development of computationally intensive applications, the demand for edge cloud computing systems has increased, creating significant challenges for edge cloud computing networks. In this paper, we consider a simple three-tier computational mod...
ver más
|
|
|
|
|
|
|
|
|
|
|
Panagiotis Gkonis, Anastasios Giannopoulos, Panagiotis Trakadas, Xavi Masip-Bruin and Francesco D?Andria
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things?IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to...
ver más
|
|
|
|
|
|
|
|
|
|
|
Elarbi Badidi
Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learn...
ver más
|
|
|
|
|
|