Results 71 to 80 of about 31,221,148 (284)

ProFed: A Benchmark for Proximity-Based Non-IID Federated Learning

Journal of Open Research Software
Federated 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, Christian Otte Ingemann, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli   +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  

Is There a Market for Organic Milk in Serbia? Insights From Integrated Choice and Latent Variable Model

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, Sanja Dzever, Bojan Matkovski, Danilo Djokic   +3 more
wiley   +1 more source

Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning

Computers
Federated 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, Ahmed Sabah Ahmed AL-Jumaili   +1 more
doaj   +1 more source

Overcoming Forgetting in Federated Learning on Non-IID Data

CoRR, 2019
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show
Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, Itai Zeitak   +6 more
openaire   +2 more sources

Consumer Demand and Market Response to Added Sugar Labeling: Evidence From the Updated Nutrition Facts Panel

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

The Effect of Sustainability Information on Consumers' Preferences for Fungus‐Resistant Grape Wine

Agribusiness, EarlyView.
ABSTRACT The demand for sustainable food production is intensifying the need to reduce the environmental and social impacts of pesticide use in agriculture. In viticulture, where dependence on pesticides is high because grapevines are susceptible to fungal diseases, fungus‐resistant grape varieties (PIWI) offer a promising strategy to reduce chemical ...
Daniel Vecchiato, Andrea Dominici, Fabio Boncinelli   +2 more
wiley   +1 more source

Adding Data Quality to Federated Learning Performance Improvement

IEEE Access
Massive 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, Solon Alves Peixoto, Valderi Reis Quietinho Leithardt, Juan Francisco de Paz Santana, Julio C. S. Dos Anjos   +4 more
doaj   +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, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning [PDF]

Logical Methods in Computer Science
In the last years, Federated learning (FL) has become a popular solution to train machine learning models in domains with high privacy concerns. However, FL scalability and performance face significant challenges in real-world deployments where data ...
Davide Domini, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli   +3 more
doaj   +1 more source