To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique, especially for large-scale model …
Recent advancements in deep neural networks (DNN) enabled various mobile deep learning applications. However, it is technically challenging to locally train a DNN model due …
H Guo, A Liu, VKN Lau - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
This article investigates the analog gradient aggregation (AGA) solution to overcome the communication bottleneck for wireless federated learning applications by exploiting the idea …
W Shi, S Zhou, Z Niu - ICC 2020-2020 IEEE International …, 2020 - ieeexplore.ieee.org
Owing to the increasing need for massive data analysis and model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training …
Federated learning (FL) enables edge devices, such as Internet of Things devices (eg, sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …
Z Qin, GY Li, H Ye - IEEE Wireless Communications, 2021 - ieeexplore.ieee.org
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful learning ability and potential applications. In …
There is an increasing interest in a new machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), and …
B McMahan, E Moore, D Ramage… - Artificial intelligence …, 2017 - proceedings.mlr.press
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language …
Z Zhao, C Feng, W Hong, J Jiang, C Jia… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless …