Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
Energy efficient fog computing for 6G enabled massive IoT: Recent trends and future opportunities
UM Malik, MA Javed, S Zeadally… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Fog computing is a promising technology that can provide storage and computational
services to future 6G networks. To support the massive Internet of Things (IoT) applications …
services to future 6G networks. To support the massive Internet of Things (IoT) applications …
Federated deep learning for zero-day botnet attack detection in IoT-edge devices
Deep learning (DL) has been widely proposed for botnet attack detection in Internet of
Things (IoT) networks. However, the traditional centralized DL (CDL) method cannot be …
Things (IoT) networks. However, the traditional centralized DL (CDL) method cannot be …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis
In this article, we present a comprehensive study with an experimental analysis of federated
deep learning approaches for cyber security in the Internet of Things (IoT) applications …
deep learning approaches for cyber security in the Internet of Things (IoT) applications …
[HTML][HTML] Federated learning in edge computing: a systematic survey
Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …
Fusion of federated learning and industrial Internet of Things: A survey
Abstract Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry
4.0 and paves an insight for new industrial era. Nowadays smart machines and smart …
4.0 and paves an insight for new industrial era. Nowadays smart machines and smart …
Twelve scientific challenges for 6g: Rethinking the foundations of communications theory
The research in the sixth generation of wireless networks needs to tackle new challenges in
order to meet the requirements of emerging applications in terms of high data rate, low …
order to meet the requirements of emerging applications in terms of high data rate, low …
Asynchronous federated learning on heterogeneous devices: A survey
Federated learning (FL) is experiencing a fast booming with the wave of distributed machine
learning. In the FL paradigm, the global model is aggregated on the centralized aggregation …
learning. In the FL paradigm, the global model is aggregated on the centralized aggregation …
Big data resource management & networks: Taxonomy, survey, and future directions
Big Data (BD) platforms have a long tradition of leveraging trends and technologies from the
broader computer network and communication community. For several years, dedicated …
broader computer network and communication community. For several years, dedicated …