Results 111 to 120 of about 6,308,360 (314)
nicolasfara/experiments-2024-ACSOS-opportunistic-federated-learning: 1.2.0
<h2><a href="https://github.com/nicolasfara/experiments-2024-ACSOS-opportunistic-federated-learning/compare/1.1.1...1.2.0">1.2.0</a> (2024-04-15)</h2> <h3>Features</h3> <ul> <li>add python modules import ...
Semantic Release Bot +3 more
core +1 more source
The Collective Power of Bacteria as a Blueprint for Emergent Intelligence
Small cells, powerful collectives. Bacteria demonstrate how sophisticated behaviors can emerge from many simple individuals working together. We explore the remarkable world of bacterial communities and the mechanisms that underpin their complex emergent behaviors, enabling impressive adaptability, robustness, and responsiveness to changing ...
Johanna A. Blee +2 more
wiley +1 more source
Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy
In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and ...
Feng Wang +2 more
doaj +1 more source
Towards Trustworthy Collaborative Machine Learning: A Blockchain-Driven Federated Approach [PDF]
openLo scopo di questa tesi è studiare come integrare la tecnologia Blockchain (BC) con il Federated Learning (FL) possa migliorare la sicurezza e l'affidabilità dei sistemi FL standard.
ANTONELLO, ILENIA
core
ABSTRACT Arthrogryposis multiplex congenita (AMC) is a group of rare congenital conditions, characterized by multiple joint contractures but may involve any body system including central nervous system. AMC is etiologically heterogeneous, with over 400 genetic and many non‐genetic causes implicated in its prenatal development.
Shahrzad Nematollahi +20 more
wiley +1 more source
Federated Learning in Data Privacy and Security
Federated learning (FL) has been a rapidly growing topic in recent years. The biggest concern in federated learning is data privacy and cybersecurity. There are many algorithms that federated models have to work on to achieve greater efficiency, security,
Dokuru Trisha Reddy +3 more
doaj +1 more source
Forest Fire Prediction Frameworks using Federated Learning and Internet of Things (IoT)
Forest fire ignition prediction is of paramount importance in safeguarding communities. Machine learning is a promising tool to enhance this. There is, however, a scarcity of publicly accessible forest fire ignition datasets. Moreover, existing platforms,
Purcell, Richard
core
Toward Quantum Federated Learning
Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process.
Chao Ren 0006 +11 more
openaire +5 more sources
The Politics of Framing the Student Problem: Inquiries Into Australian Civics Education, 2006–2024
ABSTRACT Recurring debates about civics, the kinds of history that should, and should not, be taught in school, and ‘standards debates’ about the ‘basics’ typically follow on the heels of recurring moral panics about the ‘declining’ state of ‘our’ education system.
Patrick O'Keeffe +2 more
wiley +1 more source
Federated Versus Central Machine Learning on Diabetic Foot Ulcer Images: Comparative Simulations
This research examines the implementation of the U-Net model within a federated learning framework, focusing on the semantic segmentation of Diabetic Foot Ulcers (DFUs) images.
Mahdi Saeedi +3 more
doaj +1 more source

