Results 81 to 90 of about 33,062,051 (187)

Cloud–Edge–End Collaborative Federated Learning: Enhancing Model Accuracy and Privacy in Non-IID Environments

Sensors
Cloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID ...
Ling Li, Lidong Zhu, Weibang Li
doaj   +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

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

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

Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data. [PDF]

Sensors (Basel), 2023
Seol M, Kim T.
europepmc   +1 more source

Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets [PDF]

, 2022
Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations.
Marcus, Völp, Maria, Fernandes, Jérémie, Decouchant, Lydia, Chen, LUCCHETTI, Federico   +4 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

DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data. [PDF]

Sensors (Basel), 2022
Lee J, Kim W.
europepmc   +1 more source

Realized Variance and IID Market Microstructure Noise [PDF]

We analyze the properties of a bias-corrected realized variance (RV) in the presence of iid market microstructure noise. The bias correction is based on the first-order autocorrelation of intraday returns and we derive the optimal sampling frequency as ...
Asger Lunde, Peter Reinhard Hansen
core  

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