Results 41 to 50 of about 34,864,804 (179)
Federated proximal learning with data augmentation for brain tumor classification under heterogeneous data distributions [PDF]
PeerJ Computer ScienceThe increasing use of electronic health records (EHRs) has transformed healthcare management, yet data sharing across institutions remains limited due to privacy concerns.
Swetha Ghanta +5 more
doaj +2 more sources
Performance gap between IID and non-IID data.
, 2020 Performance gap between IID and non-IID data.
Zeng Fu (8727135) +5 more
core +1 more source
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 +4 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 +4 more
doaj +1 more source
Ensemble Federated Adversarial Training with Non-IID data
CoRR, 2021 Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness.
Shuang Luo +3 more
openaire +2 more sources
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 +5 more
doaj +1 more source
A Privacy-Preserving Collaborative Federated Learning Framework for Detecting Retinal Diseases
IEEE AccessThe 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 +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 SciencesPneumonia 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
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 +2 more
openaire +4 more sources