Results 51 to 60 of about 96,144 (271)

A Review of Research on Secure Aggregation for Federated Learning

Future Internet
Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods ...
Xing Zhang, Yuexiang Luo, Tianning Li
doaj   +1 more source

Evaluation of Digital Technologies for Home‐Based Assessment in People With Amyotrophic Lateral Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Digital technologies hold promise for transforming healthcare by enhancing personalized treatments and offer valuable opportunities to improve patient care. Here, we evaluated several novel, self‐administered, home‐based, digital endpoints for their association with corresponding conventional standard clinical measures (primary) in ...
Arne Mueller, Vanessa Vallejo, Mónica Povedano Panadés, Orla Hardiman, Véronique Danel‐Brunaud, Senda Ajroud‐Driss, Philippe Couratier, Jeremy Shefner, Anna Gokey, Mike DiCesare, Erin Lennox, Jens Praestgaard, Lucie Brujin, Peggy Allred, Ram Miller   +14 more
wiley   +1 more source

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

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

Blockchained On-Device Federated Learning [PDF]

IEEE Communications Letters, 2020
to appear in IEEE Communications ...
Hyesung Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim   +3 more
openaire   +3 more sources

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

Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning

EURASIP Journal on Audio, Speech, and Music Processing, 2023
Speaker recognition, the process of automatically identifying a speaker based on individual characteristics in speech signals, presents significant challenges when addressing heterogeneous-domain conditions.
Zhiyong Chen, Shugong Xu
doaj   +1 more source

Partial Federated Learning

CoRR
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns. Typically, FL is trained with the assumption that no part of the user data can be egressed from the edge.
Tiantian Feng, Anil Ramakrishna, Jimit Majmudar, Charith Peris, Jixuan Wang, Clement Chung, Richard S. Zemel, Morteza Ziyadi, Rahul Gupta 0001   +8 more
openaire   +2 more sources

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

Formation of Stable Amorphous Calcium Phosphate and Collagen Assemblies by a Versatile Spray‐Drying Approach

Advanced Functional Materials, EarlyView.
Amorphous calcium phosphate (ACP) microparticles with long‐term and thermal stability are prepared with or without collagen using a scalable one‐pot spray‐drying process. Under simulated physiological conditions, they crystallize into biomimetic bone mineral and, when combined with collagen, form extrudable, fibrillar bone‐like 3D constructs.
Camila Bussola Tovani, Nessma Bennaceur, Celina Benkhellat, Tristan Georges, Clara Doisneau, Vaskar Sardhalia, Thierry Azaïs, Flora Lemaire, Halima Kerdjoudj, Mohamed Selmane, Capucine Sassoye, Marie Albéric, Cédric Boissière, Nadine Nassif   +13 more
wiley   +1 more source