Results 61 to 70 of about 31,140,529 (283)
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data
CoRR, 2022 Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL. Existing data augmentation methods based on federated generative models or raw data sharing strategies for solving the non-IID problem still suffer from low performance ...
Shaoming Duan +5 more
openaire +2 more sources
Linear Scalarization for Byzantine-robust learning on non-IID data
, 2022 In this work we study the problem of Byzantine-robust learning when data among clients is heterogeneous. We focus on poisoning attacks targeting the convergence of SGD.
Errami, Latifa, Bergou, El Houcine
core
Non-IID Learning for Recommendation, Time Series and Hashing [PDF]
, 2023 University of Technology Sydney. Faculty of Engineering and Information Technology.For several decades, the independent and identically distributed (short for IID) assumption has laid the foundation of data learning, simplifying real-world data's ...
Zhang, Qi
core +1 more source
Consumer Preferences for Craft Beer: The Interplay of Localness and Advertising Language
Agribusiness, EarlyView.ABSTRACT This study explores the influence of the language of the label, origin of production, and origin of brewing ingredients on Croatian consumers' preferences and willingness to pay for organic craft beer. Employing an online survey and a choice experiment among 223 Croatian alcohol consumers, we find that while there's a willingness to pay a ...
Marija Cerjak +2 more
wiley +1 more source
Mitigating non-IID data impact in federated learning with entropy
, 2023 Algoritmos de Machine Learning (ML) possibilitam processar um conjunto de dados de entradas para gerar coeficientes que ajustem a saída a um resultado previamente conhecido, como menor erro possível, fazendo com que seja possível reconhecer e extrair ...
Orlandi, Fernanda Cavalheiro
core +1 more source
Mathematics, 2023 Federated learning (FL) offers the possibility of collaboration between multiple devices while maintaining data confidentiality, as required by the General Data Protection Regulation (GDPR).
Iuliana Bejenar +3 more
doaj +1 more source
Agribusiness, EarlyView.ABSTRACT This paper explores Swedish consumers' protein preferences by estimating the willingness‐to‐pay (WTP) for minced meat and plant‐based proteins in pasta sauce from an in‐store experiment (n = 206) and an online discrete choice experiment (n = 517). On average, the WTP was highest for minced meat.
Emilia Mattsson +3 more
wiley +1 more source
IEEE AccessIn 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 +1 more
doaj +1 more source
A Distributed Privacy Preserved Federated Learning Approach for Revolutionizing Pneumonia Detection in Isolated Heterogenous Data Silos [PDF]
International Journal of Mathematical, Engineering and Management SciencesPneumonia is a respiratory lung contamination that ranges in severity from mild to lethal outcomes. The analysis of tomographic images is the most significant method of pneumonia detection.
Shagun Sharma, Kalpna Guleria
doaj +1 more source
Privacy Protection in Prosumer Energy Management Based on Federated Learning
IEEE Access, 2021 With the booming development of prosumers, there is an urgent need for a prosumer energy management system to take full advantage of the flexibility of prosumers and take into account the interests of other parties.
Yunfeng Li +3 more
doaj +1 more source