Results 41 to 50 of about 31,221,148 (284)
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
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
DCFL: Non-IID awareness Data Condensation aided Federated Learning [PDF]
, 2023 Federated learning is a decentralized learning paradigm wherein a central server trains a global model iteratively by utilizing clients who possess a certain amount of private datasets. The challenge lies in the fact that the client side private data may
Sun, YaFeng, Sha, Shaohan
core
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Advanced Science, EarlyView.Streptococcal mannose phosphotransferase system component IID (Man‐PTSIID) is identified as a novel RANK‐binding osteoclastogenic factor. By directly binding to RANK and activating NF‐κB independently of TLR2, Man‐PTSIID drives osteoclastogenesis and inflammatory bone destruction, uncovering an unexpected microbial mechanism underlying streptococcal ...
Chaeyeon Park +13 more
wiley +1 more source
Low Precision Decentralized Distributed Training over IID and non-IID Data [PDF]
, 2022 Decentralized distributed learning is the key to enabling large-scale machine learning (training) on edge devices utilizing private user-generated local data, without relying on the cloud.
Aketi, Sai Aparna +2 more
core +1 more source
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 +6 more
doaj +1 more source