Results 51 to 60 of about 6,308,360 (314)

A Review of Federated Learning in Agriculture

Sensors, 2023
Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems.
Krista Rizman Zalik, Mitja Zalik
openaire   +6 more sources

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Poster Abstract: Federated Learning for Speech Emotion Recognition Applications

, 2020
Privacy concerns are considered one of the major challenges in the applications of speech emotion recognition (SER) as it involves the complete sharing of speech data, which can bring threatening consequences to people’s lives.
Raja Jurdak, Siddique Latif, Latif, Siddique, Rana, Rajib, Sara Khalifa, Khalifa, Sara, Jurdak, Raja, Rajib Rana   +7 more
core   +1 more source

Personalized Federated Learning With a Graph

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new model personalization mechanisms while simply implementing knowledge sharing by aggregating models from all clients, regardless of their relation graph.
Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou 0001, Jing Jiang 0002   +4 more
openaire   +2 more sources

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Benchmark for Personalized Federated Learning

IEEE Open Journal of the Computer Society
Federated learning is a distributed machine learning approach that allows a single server to collaboratively build machine learning models with multiple clients without sharing datasets.
Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka   +3 more
doaj   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Federated Identity Management [PDF]

, 2009
. This paper addresses the topic of federated identity management. It discusses in detail the following topics: what is digital identity, what is identity management, what is federated identity management, Kim Camerons 7 Laws of Identity, how can we ...
Chadwick, David W., David W. Chadwick
core   +1 more source

From distributed machine learning to federated learning: In the view of data privacy and security

, 2022
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server.
Zhou, Wanlei, Wang, Wei, Zhu, Tianqing, Di Wu, Wanlei Zhou, Sheng Shen, Shen, Sheng, Tianqing Zhu, Wei Wang, Wu, Di   +9 more
core   +1 more source

PeFLL: Personalized Federated Learning by Learning to Learn

, 2023
We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation ...
Scott, Jonathan A, Zakerinia, Hossein, Lampert, Christoph   +2 more
openaire   +4 more sources