Results 71 to 80 of about 72,357 (237)

Lifecycle‐Based Governance to Build Reliable Ethical AI Systems

Systems Research and Behavioral Science, EarlyView.
ABSTRACT Artificial intelligence (AI) systems represent a paradigm shift in technological capabilities, offering transformative potential across industries while introducing novel governance and implementation challenges. This paper presents a comprehensive framework for understanding AI systems through three critical dimensions: trustworthiness ...
Maikel Leon
wiley   +1 more source

FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing

Defence Technology
Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments ...
Kangning Yin, Xinhui Ji, Yan Wang, Zhiguo Wang   +3 more
doaj   +1 more source

Vertical Federated Learning: A Structured Literature Review

, 2023
Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations.
Khan, Afsana, Thij, Marijn ten, Wilbik, Anna   +2 more
core  

Federated Learning for Cloud and Edge Security: A Systematic Review of Challenges and AI Opportunities

Electronics
The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) and ...
Latifa Albshaier, Seetah Almarri, Abdullah Albuali   +2 more
semanticscholar   +1 more source

Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation

IEEE Transactions on Parallel and Distributed Systems, 2023
Federated learning (FL), an attractive and promising distributed machine learning paradigm, has sparked extensive interest in exploiting tremendous data stored on ubiquitous mobile devices. However, conventional FL suffers severely from resource heterogeneity, as clients with weak computational and communication capability may be unable to complete ...
Qian Chen, Zilong Wang, Jiawei Chen, Haonan Yan, Xiaodong Lin   +4 more
openaire   +2 more sources

Computational intelligence model for predicting the compressive strength of FRP‐confined concrete column

Structural Concrete, EarlyView.
Abstract Fiber reinforced polymer (FRP) wrapping technology is commonly used to enhance the compressive strength (CS) of reinforced concrete (RC) members. Accurate prediction of the compressive strength of FRP‐confined concrete columns is crucial for optimizing structural design and helps reduce the time and costs associated with physical testing ...
XuanRui Yu, Nima Khodadadi, Anxiang Song, Tianyu Hu, Yang Yu, Antonio Nanni   +5 more
wiley   +1 more source

Federated Neural Architecture Search

, 2020
To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data. However, training over decentralized data makes the design of neural architecture quite difficult as it ...
Bian, Kaigui, Huang, Gang, Liu, Xuanzhe, Mei, Qiaozhu, Xu, Mengwei, Zhao, Yuxin   +5 more
core  

Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global Insights

, 2023
In the field of federated learning, addressing non-independent and identically distributed (non-i.i.d.) data remains a quintessential challenge for improving global model performance.
Hu, Ke, Qiu, WeiDong, Tang, Peng
core  

SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare

Information
Federated Learning (FL) is emerging as an encouraging paradigm for AI model training in healthcare that enables collaboration among institutions without revealing sensitive information.
Alba Amato, Dario Branco
doaj   +1 more source

Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help ...
Chandramohan Dhasaratha, Mohammad Kamrul Hasan, Shayla Islam, Shailesh Khapre, Salwani Abdullah, Taher M. Ghazal, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Nguyen Vo, Md Akhtaruzzaman   +9 more
wiley   +1 more source