Results 71 to 80 of about 9,950,038 (297)

Volatility analysis and forecasting of vegetable prices using an ARMA‐GARCH model: An application of the CF filter and seasonal adjustment method to Korean green onions

Agribusiness, EarlyView.
Abstract The vegetable market experiences significant price fluctuations due to the complex interplay of trend, cyclical, seasonal, and irregular factors. This study takes Korean green onions as an example and employs the Christiano–Fitzgerald filter and the CensusX‐13 seasonal adjustment methods to decompose its price into four components: trend ...
Yiyang Qiao, Byeong‐il Ahn
wiley   +1 more source

Coordinated Multicasting with Opportunistic User Selection in Multicell Wireless Systems

, 2015
Physical layer multicasting with opportunistic user selection (OUS) is examined for multicell multi-antenna wireless systems. By adopting a two-layer encoding scheme, a rate-adaptive channel code is applied in each fading block to enable successful ...
Chang, Tsung-Hui, Hong, Y. -W. Peter, Lee, Chia-Han, Li, Wei-Chiang   +3 more
core   +1 more source

Differentially Private Federated Clustering Over Non-IID Data

IEEE Internet of Things Journal
34 pages, 4 figures, 1 ...
Yiwei Li, Shuai Wang, Chong-Yung Chi, Tony Q. S. Quek   +3 more
openaire   +2 more sources

Foreign labor, peer‐networking and agricultural efficiency in the Italian dairy sector

Agribusiness, EarlyView.
Abstract While the presence of immigrants in the agricultural sector is widely acknowledged, the empirical evidence on its economic consequences is lacking, especially from a microeconomic perspective. Using the Farm Accountancy Data Network panel data for Italian dairy farms in the period 2008–2018, the present study investigates the relationship ...
Federico Antonioli, Simone Severini, Mauro Vigani   +2 more
wiley   +1 more source

Federated Learning for Sentiment Analysis in Presence of Non-IID Data: Sensitivity of Deep Learning Models

IEEE Access
In sentiment analysis, data are commonly distributed across many devices, and traditional machine learning requires transferring these data to a central location exposing data to security and privacy risks. Federated Learning (FL) avoids this transfer by
Davoud Gholamiangonabadi, Katarina Grolinger   +1 more
doaj   +1 more source

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

Diagnostics, 2023
In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts.
Chetna Gupta, Vikas Khullar, Nitin Goyal, Kirti Saini, Ritu Baniwal, Sushil Kumar, Rashi Rastogi   +6 more
doaj   +1 more source

Determinants of local food producer participation in state‐sponsored marketing programs: Evidence from Missouri

Agribusiness, EarlyView.
Abstract This study examines producer participation choices considering a variety of potential benefits linked to state‐sponsored marketing programs, using a real choice dataset of farmers in Missouri. Multinomial logit models are employed to predict determinants of farmer enrollment in three tiers of the Missouri Grown local food marketing program ...
Lan Tran, Ye Su, Laura McCann
wiley   +1 more source

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Applied Sciences
Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges.
Basmah Alotaibi, Fakhri Alam Khan, Sajjad Mahmood   +2 more
doaj   +1 more source

Global Layers: Non-IID Tabular Federated Learning

, 2023
Data heterogeneity between clients remains a key challenge in Federated Learning (FL), particularly in the case of tabular data. This work presents Global Layers (GL), a novel partial model personalization method robust in the presence of joint distribution $P(X,Y)$ shift and mixed input/output spaces $X \times Y$ across clients.
openaire   +2 more sources

Ensemble Federated Adversarial Training with Non-IID data

, 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.
Luo, Shuang, Zhu, Didi, Li, Zexi, Wu, Chao   +3 more
openaire   +2 more sources