Results 81 to 90 of about 34,864,804 (179)

TFedSec-HI: Transformer-Driven Federated Security for IoT-Enabled Healthcare Industry 5.0 on Non-IID Data

IEEE Internet of Things Journal
The Internet of Things (IoT) enhances the healthcare industry 5.0 by enabling connected devices and data-driven treatments, but it also introduces cyber threats, such as data breaches, and unauthorized access.
Yue Zhao, Farhan Ullah, Khalid Mahmood, Jawad Ahmad, Ali Kashif Bashir, Nazik Alturki   +5 more
semanticscholar   +1 more source

The infectious intestinal disease study of England: a prospective evaluation of symptoms and health care use after an acute episode [PDF]

, 2003
The sequelae of Infectious Intestinal Disease (IID) in a population-based sample of cases and matched controls were investigated for a period of 3 months following the initial infection.
Rodrigues, L.C., Hudson, M.J., Cowden, JM, Wheeler, JG, Cumberland, P., Rodrigues, LC, Tompkins, D.S., Sethi, D., IID Study Executive, Hudson, MJ, Roderick, PJ, Tompkins, DS, Roderick, P.J., Roberts, JA, Wheeler, J.G., Roberts, J.A., Cumberland, P, Cowden, J.M., Sethi, D   +18 more
core   +1 more source

Analysis and Improvement of Entropy Estimators in NIST SP 800-90B for Non-IID Entropy Sources

IACR Transactions on Symmetric Cryptology, 2017
Random number generators (RNGs) are essential for cryptographic applications. In most practical applications, the randomness of RNGs is provided by entropy sources.
Shuangyi Zhu, Yuan Ma, Tianyu Chen, Jingqiang Lin, Jiwu Jing   +4 more
doaj   +1 more source

Differentially Private Federated Learning on Non-iid Data: Convergence Analysis and Adaptive Optimization

IEEE Transactions on Knowledge and Data Engineering
Federated learning (FL) has attracted increasing attention in recent years due to its data privacy preservation and great applicability to large-scale user scenarios. However, when FL faces numerous clients, it is inevitable to emerge the non-independent
Lin Chen, Xiao-Feng Ding, Zhi-Feng Bao, Pan Zhou, Hai Jin   +4 more
semanticscholar   +1 more source

Federated Loss Exploration for Improved Convergence on Non-IID Data

Internò C, Olhofer M, Jin Y, Hammer B. Federated Loss Exploration for Improved Convergence on Non-IID Data. In: 2024 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN).
Hammer, Barbara ; https://orcid.org/, Jin, Yaochu, Internò, Christian, Olhofer, Markus   +3 more
core   +1 more source

Federated PAC-Bayesian Learning on Non-IID data [PDF]

, 2023
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
Zhao, Zihao, Liu, Yang, Ding, Wenbo, Zhang, Xiao-Ping   +3 more
core  

Addressing Non-IID with Data Quantity Skew in Federated Learning

Information
Non-IID is one of the key challenges in federated learning. Data heterogeneity may lead to slower convergence, reduced accuracy, and more training rounds.
Narisu Cha, Long Chang
doaj   +1 more source

Global Convergence of Continual Learning on Non-IID Data

CoRR
Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical aspect remains under-explored. Recently, a few investigations have considered the theory of continual learning only for linear regressions, establishes the results based on ...
Fei Zhu 0004, Yujing Liu, Wenzhuo Liu, Zhaoxiang Zhang 0001   +3 more
openaire   +3 more sources

Beyond IID: Learning to combine Non-IID metrics for vision tasks

, 2017
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Metric learning has been widely employed, especially in various computer vision tasks, with the fundamental assumption that all samples (e.g.
Gao, Y, Shi, Yinghuan, Gao, Yang, Li, W, Cao, L, Shen, Dinggang, Li, Wenbin, Shi, Y, Shen, D, Cao, Longbing, Longbing Cao, Yang Gao, Yinghuan Shi, Wenbin Li, Dinggang Shen   +14 more
core   +1 more source

Weighted Ensemble Distillation in Federated Learning with Non-IID Data [PDF]

, 2022
Federated distillation (FD) is a novel algorithmic idea for federated learning (FL) that allows clients to use heterogeneous model architectures. This is achieved by distilling aggregated local model predictions on an unlabeled auxiliary dataset into ...
Eriksson, Oscar
core