Results 41 to 50 of about 12,973 (156)

FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing

Applied Sciences, 2023
Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally.
Yankai Lv, Haiyan Ding, Hao Wu, Yiji Zhao, Lei Zhang   +4 more
doaj   +1 more source

Federated XGBoost on Sample-Wise Non-IID Data

CoRR, 2022
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution of the local parties involved in the training process.
Katelinh Jones, Yuya Jeremy Ong, Yi Zhou 0015, Nathalie Baracaldo   +3 more
openaire   +2 more sources

FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System

IEEE Access, 2022
In recent years, the data available from IoT devices have increased rapidly. Using a machine learning solution to detect faults in these devices requires the release of device data to a central server.
Raneen Younis, Marco Fisichella
doaj   +1 more source

A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases

IEEE Access
The rapid advancement in technology has simplified human life and provides convenience. However, this convenience has led to many lifestyle diseases like diabetes and obesity.
Seema Gulati, Kalpna Guleria, Nitin Goyal, Ahmad Ali AlZubi, Angel Kuc Castilla   +4 more
doaj   +1 more source

Peer-to-peer deep learning with non-IID data

Expert Systems with Applications, 2023
Collaborative training of deep neural networks using edge devices has attracted substantial research interest recently. The two main architecture approaches for the training process are centrally orchestrated Federated Learning and fully decentralized peer-to-peer learning.
Robert Sajina, Nikola Tankovic, Ivo Ipsic   +2 more
openaire   +3 more sources

A Collaborative Privacy Preserved Federated Learning Framework for Pneumonia Detection using Diverse Chest X-ray Data Silos [PDF]

International Journal of Mathematical, Engineering and Management Sciences
Pneumonia detection from chest X-rays remains one of the most challenging tasks in the traditional centralized framework due to the requirement of data consolidation at the central location raising data privacy and security concerns.
Shagun Sharma, Kalpna Guleria
doaj   +1 more source

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Agronomy, 2023
Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress.
Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Rama Gautam, Fahad Alblehai, Magdy Elghatwary, Aman Singh   +6 more
doaj   +1 more source

Federated Conditional Variational Auto Encoders for Cyber Threat Intelligence: Tackling Non-IID Data in SDN Environments

IEEE Access
Federated Learning is a promising paradigm for sharing Cyber Threat Intelligence (CTI) without privacy issues by leveraging the cross-silos data in Software Defined Networking (SDN).
Syed Hussain Ali Kazmi, Rosilah Hassan, Faizan Qamar, Kashif Nisar, Mohammed Azmi Al-Betar   +4 more
doaj   +1 more source

Interictal Discharge Pattern in Preschool-Aged Children With Tuberous Sclerosis Complex Before and After Resective Epilepsy Surgery

Frontiers in Neurology, 2022
ObjectiveTo analyze the interictal discharge (IID) patterns on pre-operative scalp electroencephalogram (EEG) and compare the changes in IID patterns after removal of epileptogenic tubers in preschool children with tuberous sclerosis complex (TSC ...
Liu Yuan, Liu Yuan, Yangshuo Wang, Shuhua Cheng, Junchen Zhang, Shaohui Zhang, Tinghong Liu, Guojun Zhang, Guojun Zhang, Shuli Liang, Shuli Liang   +10 more
doaj   +1 more source

Aggregation Methods Based on Quality Model Assessment for Federated Learning Applications: Overview and Comparative Analysis

Mathematics, 2023
Federated learning (FL) offers the possibility of collaboration between multiple devices while maintaining data confidentiality, as required by the General Data Protection Regulation (GDPR).
Iuliana Bejenar, Lavinia Ferariu, Carlos Pascal, Constantin-Florin Caruntu   +3 more
doaj   +1 more source