Y Yang, Z Zhang, Q Yang - IEEE Journal on Selected Areas in …, 2021 - ieeexplore.ieee.org
Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a …
Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the …
S AbdulRahman, H Tout… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Driven by privacy concerns and the visions of deep learning, the last four years have witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …
Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and …
There is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based …
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on enabling software and hardware platforms, protocols, real-life applications and use …
Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and …
Federated Learning (FL) has been considered as an appealing framework to tackle data privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …
H Liu, X Yuan, YJA Zhang - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …