|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
|
Mikael Sabuhi, Petr Musilek and Cor-Paul Bezemer
As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges a...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ishaani Priyadarshini
The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and funct...
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
|
|
|
|
|
|
|
|
|
|
|
Ying-Hsun Lai, Shin-Yeh Chen, Wen-Chi Chou, Hua-Yang Hsu and Han-Chieh Chao
Federated learning trains a neural network model using the client?s data to maintain the benefits of centralized model training while maintaining their privacy. However, if the client data are not independently and identically distributed (non-IID) becau...
ver más
|
|
|
|
|
|
|
|
|
|
|
Pin-Hung Juan and Ja-Ling Wu
In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non...
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
|
|
|
|
|
|
|
|
|
|
|
Hadeel Alrubayyi, Moudy Sharaf Alshareef, Zunaira Nadeem, Ahmed M. Abdelmoniem and Mona Jaber
The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, w...
ver más
|
|
|
|
|
|