Results 71 to 80 of about 31,140,529 (283)
Adaptive Federated Learning on Non-IID Data With Resource Constraint
, 2022 Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data.
Zhan, Yufeng +6 more
core +1 more source
Assessing the Impact of Promotions on Consumer Purchasing Behavior During Crises
Agribusiness, EarlyView.ABSTRACT Understanding how households modify their food expenditure decisions during times of crisis is essential because consumer purchasing behavior frequently changes during these times. This study looks at these behavioral shifts during the COVID‐19 pandemic, concentrating on how price sensitivity and response to sales promotions changed over the ...
Wafa Mehaba, José María Gil
wiley +1 more source
ProFed: A Benchmark for Proximity-Based Non-IID Federated Learning
Journal of Open Research SoftwareFederated Learning (FL) has emerged as a key paradigm in machine learning but its performance often deteriorates under non-independent and identically distributed (non-IID) client data.
Davide Domini +4 more
doaj +1 more source
Non-IID representation learning on complex categorical data [PDF]
, 2019 University of Technology Sydney. Faculty of Engineering and Information Technology.Learning complex categorical data requires proper vector or metric representations of the intricate characteristics of that data.
Zhu, Chengzhang
core
Agribusiness, EarlyView.ABSTRACT Brazil and the United States account for more than 40% of global poultry exports, with China and South Korea among their major destination markets. This study examines price transmission and market linkages between Brazil and the United States using monthly poultry export price data from January 1990 to December 2024. It also assesses which of
Khondoker Abdul Mottaleb +2 more
wiley +1 more source
Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning
ComputersFederated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem ...
Huda Kadhim Tayyeh +1 more
doaj +1 more source
Agribusiness, EarlyView.ABSTRACT Past growth in the global organic market has been concentrated in high‐income countries, while in middle‐income countries such as Serbia the organic market remains nascent and characterized by a sparse assortment of organic products, high retail premia and limited evidence on consumer preferences and their drivers.
Milan Tatic +3 more
wiley +1 more source
Agribusiness, EarlyView.ABSTRACT This paper examines how mandatory disclosure of added sugar content on the updated U.S. Nutrition Facts Panel (NFP) affects consumer demand and market outcomes. Using NielsenIQ Retail Scanner Data (2015–2020) and a random coefficient discrete choice model, we estimate how added sugar labeling influences purchasing behavior in yogurt and cookie
Yuxiang Zhang, Yizao Liu
wiley +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Adding Data Quality to Federated Learning Performance Improvement
IEEE AccessMassive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (
Ernesto Gurgel Valente Neto +4 more
doaj +1 more source