Results 101 to 110 of about 34,864,804 (179)

FedBound a boundary aware optimization strategy for federated medical image segmentation under non IID data

Discover Computing
Medical image segmentation is essential for computer-aided diagnosis and treatment planning. Privacy constraints impede centralized training using medical images from diverse healthcare organizations.
Divyansh Pandey, Aaryan Gupta, Varun Tiwari, Jaydeep Kishore, Amit Kumar Bairwa   +4 more
doaj   +1 more source

FedDB: A Federated Learning Approach Using DBSCAN for DDoS Attack Detection

Applied Sciences
The rise of Distributed Denial of Service (DDoS) attacks on the internet has necessitated the development of robust and efficient detection mechanisms. DDoS attacks continue to present a significant threat, making it imperative to find efficient ways to ...
Yi-Chen Lee, Wei-Che Chien, Yao-Chung Chang   +2 more
doaj   +1 more source

Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks

Scientific Reports
Multi-institutional healthcare Internet of Things (IoT) networks face a core challenge between combined intrusion detection and patient data privacy.
Sudhakar Sengan, Chin-Shiuh Shieh
doaj   +1 more source

Autoencoder-based clustered federated learning with importance-aware aggregation for sentiment analysis on non-IID data

Machine Learning with Applications
Sentiment analysis increasingly faces challenges with traditional centralized machine learning as data are often decentralized and transferring them to a central location raises privacy and security risks. Federated learning (FL) mitigated these risks by
Davoud Gholamiangonabadi, Muhammad Umair Danish, Zhenghua Chen, Katarina Grolinger   +3 more
doaj   +1 more source

Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data [PDF]

The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving the privacy of clients' data.
Morafah, Mahdi, Louizos, Christos, Reisser, Matthias, Lin, Bill   +3 more
core  

Feature Matching Data Synthesis for Non-IID Federated Learning

, 2023
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server.
Sun, Yuchang, Mao, Yuyi, Shao, Jiawei, Wang, Jessie Hui, Li, Zijian, Zhang, Jun   +5 more
core  

Chromatic PAC-Bayes Bounds for Non-IID Data.

, 2009
Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned with \iid data, and it is particularly so for margin classifiers. However, there are many practical cases where the training data show some dependencies and where the traditional \iid assumption does not apply.
Ralaivola, Liva, Szafranski, Marie, Stempfel, Guillaume   +2 more
openaire   +2 more sources

Split Averaging: Bridging the Heterogeneity Gap in Clients Data for Federated Learning

IEEE Access
Federated Learning (FL) has gained significant prominence to overcome the issue of data silos in various domains. However, since its introduction FL has been confronted with the presence of Non-Independent and Identically Distributed (Non-IID) data ...
Sajjad Khan, Nikita Karetnikov, Muhammad Habib Ur Rehman, Davor Svetinovic   +3 more
doaj   +1 more source

Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning approach

Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning ...
Mohamed Abdel-Basset (5152634), Hossam Hawash (18173107), Nour Moustafa (9644231), Mohamed Abd Elfattah (18173110), Imran Razzak (13074405)   +4 more
core  

Evaluation of Remotely Sensed Inundation Data Sets to Estimate Flood‐Associated Emergency Department Visits After Hurricane Harvey

GeoHealth
Floods can increase the risk of adverse health outcomes through multiple pathways, including contamination of food and water. Remotely sensed (RS) inundation extents can help identify regions with expected heightened flood‐related health risks, but ...
Balaji Ramesh, Julia M. Gohlke, Benjamin Zaitchik, Ayaz Hyder, Jeffrey J. Wing, Gia Barboza‐Salerno, Samarth Swarup   +6 more
doaj   +1 more source