Results 41 to 50 of about 14,006 (158)

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

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 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

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   +4 more sources

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

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

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

FedProc: Prototypical contrastive federated learning on non-IID data

Future Generation Computer Systems, 2023
Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of all clients are not independent and identically distributed (i.e., non-IID), it is challenging to implement this form of efficient collaborative learning.
Xutong Mu, Yulong Shen 0001, Ke Cheng 0001, Xueli Geng, Jiaxuan Fu, Tao Zhang 0029, Zhiwei Zhang 0004   +6 more
openaire   +2 more sources

Federated multimodal malware classification under non-IID data

Cybersecurity
Malware data in real-world cybersecurity applications are typically distributed across multiple organizations, and privacy, security, and compliance constraints prevent these data from being shared with a central server.
Shaohua Liu, Wenbo Zhang, Tingting Yang, Guanghao Li, Lihua Zhang   +4 more
doaj   +1 more source

Federated PAC-Bayesian Learning on Non-IID Data

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local
Zihao Zhao 0001, Yang Liu 0165, Wenbo Ding 0001, Xiao-Ping Zhang 0003   +3 more
openaire   +4 more sources