Results 111 to 120 of about 72,357 (237)

Revealing the Effects of Data Heterogeneity in Federated Learning Regression Models for Short-Term Solar Power Forecasting

IEEE Access
Accurate short-term power forecasting is crucial for the successful commercialization of solar energy, helping to prevent financial losses in energy markets.
Robin Nachtigall, Marc Leon Haller, Andreas Wagner, Viktor Walter   +3 more
doaj   +1 more source

RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

, 2018
Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions.
Ferrer, Eduardo Castelló, Hardjono, Thomas, Pentland, Alex, Rudovic, Ognjen   +3 more
core  

Byzantine-Robust Decentralized Federated Learning [PDF]

Conference on Computer and Communications Security
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system follows the server-assisted architecture (server-assisted FL), where the ...
Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia Liu, Songtao Lu, Yuchen Liu, N. Gong   +7 more
semanticscholar   +1 more source

GRADE‐Based Clinical Practice Guidelines for Emergency Department Delirium Risk Stratification, Screening, and Brain Imaging in Older Patients With Suspected Delirium

Academic Emergency Medicine, Volume 33, Issue 2, February 2026.
ABSTRACT Objectives This portion of the Geriatric Emergency Department (GED) Guidelines 2.0 focuses on delirium in the emergency department (ED). Methods A multidisciplinary group applied the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and develop recommendations related to ...
Sangil Lee, Danya Khoujah, Debra Eagles, Maura Kennedy, Alexander X. Lo, Christian H. Nickel, Glenn Arendts, Luna Ragsdale, Justine Seidenfeld, Kerstin de Wit, Ines Luciani‐Mcgillivray, Christopher R. Carpenter, Shan W. Liu, on behalf of the Geriatric Emergency Department Delirium Guidelines 2.0 Writing Team   +13 more
wiley   +1 more source

Federated Multi‐Source Data Fusion for Semi‐Supervised Fault Detection in District Heating Substations

Expert Systems, Volume 43, Issue 2, February 2026.
ABSTRACT Fault detection in district heating (DH) substations is critical for energy efficiency and reliability. However, it is challenged by scarce fault labels, low‐frequency data, privacy concerns, and battery‐constrained gateways. We propose a novel hybrid semi‐supervised federated domain adaptation architecture for fault detection in DH.
Jonne van Dreven, Sadi Alawadi, Abbas Cheddad, Ahmad Nauman Ghazi, Jad Al Koussa, Dirk Vanhoudt   +5 more
wiley   +1 more source

A personalized federated learning method based on the residual multi-head attention mechanism

Journal of King Saud University: Computer and Information Sciences
Federated Learning (FL) is a distributed machine learning technique for training machine learning models across multiple clients collaboratively. It allows multiple local devices to cooperatively train global models without compromising data privacy or ...
Zhaobin Li, Zixuan Zhong, Peiliang Zuo, Hong Zhao   +3 more
doaj   +1 more source

RingSFL: An Adaptive Split Federated Learning Towards Taming Client Heterogeneity

IEEE Transactions on Mobile Computing
Federated learning (FL) has gained increasing attention due to its ability to collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt to client heterogeneity, leading to a degradation in training efficiency due to ...
Jinglong Shen, Nan Cheng, Xiucheng Wang, Feng Lyu, Wenchao Xu, Zhi Liu, Khalid Aldubaikhy, Xuemin Shen   +7 more
semanticscholar   +1 more source

Federated learning: Overview, strategies, applications, tools and future directions

Heliyon
Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data.
Betul Yurdem, M. Kuzlu, M. K. Gullu, Ferhat Ozgur Catak, Maliha Tabassum   +4 more
semanticscholar   +1 more source

A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection

IEEE Transactions on Machine Learning in Communications and Networking
In this research, we proposed a novel anomaly detection system (ADS) that integrates federated learning (FL) with blockchain for resource-constrained IoT.
Van-Doan Nguyen, Abebe Diro, Naveen Chilamkurti, Will Heyne, Khoa Tran Phan   +4 more
doaj   +1 more source

Privacy and Security Challenges in Federated Learning for UAV Systems: A Systematic Review

IEEE Access
Unmanned Aerial Vehicles have become indispensable assets across various sectors, leveraging their mobility and data collection capabilities. However, privacy and security concerns have fueled interest in Federated Learning as a potential solution ...
Ahmed Saleh Sulaiman Al Farsi, Ajmal Khan, Muhammad Rizwan Mughal, Mohammed M. Bait-Suwailam   +3 more
doaj   +1 more source