Results 31 to 40 of about 6,308,360 (314)

A federated learning algorithm using parallel-ensemble method on non-IID datasets

Complex & Intelligent Systems, 2023
Traditional federated learning algorithms suffer from considerable performance reduction with non-identically and independently distributed datasets. This paper proposes a federated learning algorithm based on parallel-ensemble learning, which improves ...
Haoran Yu, Chang Wu, Haixin Yu, Xuelin Wei, Siyan Liu, Ying Zhang   +5 more
doaj   +1 more source

A Privacy-preserving and Communication-Efficient Federated Learning solution for Industrial Applications [PDF]

, 2023
openThere has been a lot of interest in privacy-preserving federated learning because of its potential to allow collaborative model training without compromising participants' privacy.
MOHAMMADI, MOHAMMADREZA
core  

Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models [PDF]

, 2023
Federated Learning offers a powerful solution for training models on data that cannot be centrally stored due to privacy concerns. However, the existing paradigm suffers from high statistical heterogeneity across clients' data, resulting in client drift ...
Camoriano, Raffaello, Fani', Eros, Ciccone, Marco, Caputo, Barbara   +3 more
core  

Agnostic Federated Learning

CoRR, 2019
A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients. We argue that, with the existing training and inference, federated models can be biased towards different clients.
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh   +2 more
openaire   +4 more sources

Semi-Federated Learning

2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020
Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need ...
Zhikun Chen, Daofeng Li, Ming Zhao 0008, Sihai Zhang, Jinkang Zhu   +4 more
openaire   +2 more sources

Federated Learning With Multichannel ALOHA [PDF]

IEEE Wireless Communications Letters, 2020
4 pages, 4 figures, IEEE WCL (accepted)
Jinho Choi 0001, Shiva Raj Pokhrel
openaire   +3 more sources

Detecting Data Poisoning Attacks in Federated Learning for Healthcare Applications Using Deep Learning

Iraqi Journal for Computer Science and Mathematics, 2023
This work presents a novel method for securing federated learning in healthcare applications, focusing on skin cancer classification. The suggested solution detects and mitigates data poisoning attacks using deep learning and CNN architecture ...
Alaa Hamza Omran, Sahar Yousif Mohammed, Mohammad Aljanabi   +2 more
doaj   +1 more source

Federated learning in food research

Journal of Agriculture and Food Research
The use of machine learning in food research is sometimes limited due to data sharing obstacles such as data ownership and privacy requirements. Federated learning is a technique to potentially alleviate these obstacles because it allows to train machine
Zuzanna Fendor, Bas H.M. van der Velden, Xinxin Wang, Andrea Jr. Carnoli, Osman Mutlu, Ali Hürriyetoğlu   +5 more
doaj   +1 more source

Hierarchical Federated Learning Algorithm Based on EMD Optimal Matching [PDF]

Jisuanji gongcheng
Federated learning allows multiple clients to cooperatively train a high-performance global model without sharing private data. In a horizontal federated learning environment involving cross-silo scenarios, the statistical heterogeneity in the ...
WU Xiaohong, LI Pei, GU Yonggen, TAO Jie
doaj   +1 more source

Federated Learning for Open Banking

, 2021
© 2020, Springer Nature Switzerland AG. Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services.
Jiang J, Long G, Tan Y, Zhang C
core   +1 more source