Results 21 to 30 of about 96,144 (271)

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

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

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

Blind Federated Edge Learning [PDF]

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

Modular Federated Learning

2022 International Joint Conference on Neural Networks (IJCNN), 2022
To be published in IEEE IJCNN 2022 ...
Kuo-Yun Liang, Abhishek Srinivasan, Juan Carlos Andresen   +2 more
openaire   +2 more sources

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

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

Coded Federated Learning [PDF]

2019 IEEE Globecom Workshops (GC Wkshps), 2019
Federated learning is a method of training a global model from decentralized data distributed across client devices. Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data.
Sagar Dhakal, Saurav Prakash, Yair Yona, Shilpa Talwar, Nageen Himayat   +4 more
openaire   +2 more sources

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