Results 21 to 30 of about 129,870 (304)

Blind Federated Edge Learning [PDF]

IEEE Transactions on Wireless Communications, 2021
submitted for publication.
Mohammad Mohammadi Amiri, Tolga M. Duman, Deniz Gunduz, Sanjeev R. Kulkarni, H. Vincent Poor Poor   +4 more
openaire   +6 more sources

Review on application progress of federated learning model and security hazard protection

Digital Communications and Networks, 2023
Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy.
Aimin Yang, Zezhong Ma, Chunying Zhang, Yang Han, Zhibin Hu, Wei Zhang, Xiangdong Huang, Yafeng Wu   +7 more
doaj   +1 more source

Federated Learning

, 2020
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality.
Yang, Qiang ECE, Liu, Yang, Cheng, Yong, Kang, Yan, Chen, Tianjian, Yu, Han   +5 more
  +5 more sources

On a Framework for Federated Cluster Analysis

Applied Sciences, 2022
Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data.
Morris Stallmann, Anna Wilbik
doaj   +1 more source

Communication-efficient federated learning [PDF]

Proceedings of the National Academy of Sciences, 2021
Significance Federated learning (FL) is an emerging paradigm that enables multiple devices to collaborate in training machine learning (ML) models without having to share their possibly private data. FL requires a multitude of devices to frequently exchange their learned model updates, thus introducing significant communication overhead ...
Chen, Mingzhe, Shlezinger, Nir, Poor, H. Vincent, Eldar, Yonina C., Cui, Shuguang   +4 more
openaire   +2 more sources

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

Applied Sciences, 2023
The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues ...
Yihang Luo, Bei Gong, Haotian Zhu, Chong Guo   +3 more
doaj   +1 more source

Accelerating federated learning via momentum gradient descent [PDF]

, 2019
Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize first-order gradient descent (GD)
Chen, Li, Chen, Yunfei, Liu, Wei, Zhang, Wenyi   +3 more
core   +2 more sources

Robust federated learning with noisy communication [PDF]

, 2019
Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server.
Ang, Fan, Chen, Li, Chen, Yunfei, Wang, Weidong, Yu, F. Richard, Zhao, Nan   +5 more
core   +2 more sources

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

JMIR Medical Informatics, 2021
BackgroundFederated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms.
Lee, Haeyun, Chai, Young Jun, Joo, Hyunjin, Lee, Kyungsu, Hwang, Jae Youn, Kim, Seok-Mo, Kim, Kwangsoon, Nam, Inn-Chul, Choi, June Young, Yu, Hyeong Won, Lee, Myung-Chul, Masuoka, Hiroo, Miyauchi, Akira, Lee, Kyu Eun, Kim, Sungwan, Kong, Hyoun-Joong   +15 more
doaj   +1 more source

Continual Local Training for Better Initialization of Federated Models

, 2020
Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy ...
Sun, Lifeng, Yao, Xin
core   +1 more source