Incentive mechanisms for federated learning: From economic and game theoretic perspective
Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …
Recent advances in artificial intelligence for wireless internet of things and cyber–physical systems: A comprehensive survey
BA Salau, A Rawal, DB Rawat - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Advances in artificial intelligence (AI) and wireless technology are driving forward the large
deployment of interconnected smart technologies that constitute cyber–physical systems …
deployment of interconnected smart technologies that constitute cyber–physical systems …
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 …
Optimal task offloading and resource allocation for C-NOMA heterogeneous air-ground integrated power Internet of Things networks
By combining information communication technology with power grid, the smart grid-
oriented Power Internet of Things (PIoT) has become a critical technology to guarantee the …
oriented Power Internet of Things (PIoT) has become a critical technology to guarantee the …
Joint training and resource allocation optimization for federated learning in UAV swarm
Unmanned aerial vehicles (UAVs) have been widely used to perform search and tracking
tasks in military and civil fields. To perform these tasks autonomously, a swarm of multiple …
tasks in military and civil fields. To perform these tasks autonomously, a swarm of multiple …
Low-latency federated learning with DNN partition in distributed industrial IoT networks
Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed
intelligence of industrial automation thanks to its capability of distributed machine learning …
intelligence of industrial automation thanks to its capability of distributed machine learning …
Distributed learning for wireless communications: Methods, applications and challenges
With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …
Efficient device scheduling with multi-job federated learning
Recent years have witnessed a large amount of decentralized data in multiple (edge)
devices of end-users, while the aggregation of the decentralized data remains difficult for …
devices of end-users, while the aggregation of the decentralized data remains difficult for …
Digital twin for federated analytics using a Bayesian approach
We are now in an information era and the volume of data is growing explosively. However,
due to privacy issues, it is very common that data cannot be freely shared among the data …
due to privacy issues, it is very common that data cannot be freely shared among the data …
Multi-job intelligent scheduling with cross-device federated learning
Recent years have witnessed a large amount of decentralized data in various (edge)
devices of end-users, while the decentralized data aggregation remains complicated for …
devices of end-users, while the decentralized data aggregation remains complicated for …