Federated dropout—A simple approach for enabling federated learning on resource constrained devices

D Wen, KJ Jeon, K Huang - IEEE wireless communications …, 2022 - ieeexplore.ieee.org
Federated learning (FL) is a popular framework for training an AI model using distributed
mobile data in a wireless network. It features data parallelism by distributing the learning …

Federated learning over wireless IoT networks with optimized communication and resources

H Chen, S Huang, D Zhang, M Xiao… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
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 …

Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking

A Li, J Sun, X Zeng, M Zhang, H Li, Y Chen - Proceedings of the 19th …, 2021 - dl.acm.org
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 …

Analog gradient aggregation for federated learning over wireless networks: Customized design and convergence analysis

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 …

Device scheduling with fast convergence for wireless federated learning

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 …

Communication-efficient federated learning

M Chen, N Shlezinger, HV Poor… - Proceedings of the …, 2021 - National Acad Sciences
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 …

Federated learning and wireless communications

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 …

Federated learning over wireless networks: Optimization model design and analysis

NH Tran, W Bao, A Zomaya… - … -IEEE conference on …, 2019 - ieeexplore.ieee.org
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 …

Communication-efficient learning of deep networks from decentralized data

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 …

Federated learning with non-IID data in wireless networks

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 …