Results 11 to 20 of about 14,543 (114)

A Graph Neural Network Based Decentralized Learning Scheme

Sensors, 2022
As an emerging paradigm considering data privacy and transmission efficiency, decentralized learning aims to acquire a global model using the training data distributed over many user devices.
Huiguo Gao, Mengyuan Lee, Guanding Yu, Zhaolin Zhou   +3 more
doaj   +1 more source

Byzantine-robust federated learning over Non-IID data

Tongxin xuebao, 2023
The malicious attacks of Byzantine nodes in federated learning was studied over the non-independent and identically distributed dataset , and a privacy protection robust gradient aggregation algorithm was proposed.A reference gradient was designed to ...
Xindi MA, Qinghua LI, Qi JIANG, Zhuo MA, Sheng GAO, Youliang TIAN, Jianfeng MA   +6 more
doaj   +2 more sources

Federated proximal learning with data augmentation for brain tumor classification under heterogeneous data distributions [PDF]

PeerJ Computer Science
The increasing use of electronic health records (EHRs) has transformed healthcare management, yet data sharing across institutions remains limited due to privacy concerns.
Swetha Ghanta, Venkata Sumanth Siddareddy, Prasanthi Boyapati, Sujit Biswas, Gandharba Swain, Ashok K. Pradhan   +5 more
doaj   +2 more sources

Federated Learning for Frequency-Modulated Continuous Wave Radar Gesture Recognition for Heterogeneous Clients

Engineering Proceedings, 2023
Federated learning (FL) is a field in distributed optimization. Therein, the collection of data and training of neural networks (NN) are decentralized, meaning that these tasks are carried out across multiple clients with limited communication and ...
Tobias Sukianto, Matthias Wagner, Sarah Seifi, Maximilian Strobel, Cecilia Carbonelli   +4 more
doaj   +1 more source

Performance Analysis of Federated Learning Algorithms for Multilingual Protest News Detection Using Pre-Trained DistilBERT and BERT

IEEE Access, 2023
Data scientists in the Natural Language Processing (NLP) field confront the challenge of reconciling the necessity for data-centric analyses with the imperative to safeguard sensitive information, all while managing the substantial costs linked to the ...
Pascal Riedel, Manfred Reichert, Reinhold Von Schwerin, Alexander Hafner, Daniel Schaudt, Gaurav Singh   +5 more
doaj   +1 more source

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

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

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

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

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