Results 191 to 200 of about 72,357 (237)

Federated Learning (FL) – Overview

LETI Transactions on Electrical Engineering & Computer Science
Explores the fundamental aspects of federated learning (FL) in the context of intrusion detection systems (IDS) within Internet of Things (IoT) networks.
M. Al-Tameemi, M. B. Hassan, S. A. Abass
semanticscholar   +2 more sources

Pain-FL: Personalized Privacy-Preserving Incentive for Federated Learning

IEEE Journal on Selected Areas in Communications, 2021
Federated learning (FL) is a privacy-preserving distributed machine learning framework, which involves training statistical models over a number of mobile users (i.e., workers) while keeping data localized.
Peng Sun, Haoxuan Che, Zhibo Wang, Yuwei Wang, Tao Wang, Liantao Wu, Huajie Shao   +6 more
semanticscholar   +2 more sources

Time-Constrained Federated Learning (FL) in Push-Pull IoT Wireless Access

IEEE Networking Letters
Training a high-quality Federated Learning (FL) model at the network edge is challenged by limited transmission resources. Although various device scheduling strategies have been proposed, it remains unclear how scheduling decisions affect the FL model ...
Van-Phuc Bui, Junya Shiraishi, P. Popovski, Shashi Raj Pandey   +3 more
semanticscholar   +3 more sources

Chain FL: Decentralized Federated Machine Learning via Blockchain

2020 Second International Conference on Blockchain Computing and Applications (BCCA), 2020
Federated learning is a collaborative machine learning mechanism that allows multiple parties to develop a model without sharing the training data. It is a promising mechanism since it empowers collaboration in fields such as medicine and banking where data sharing is not favorable due to legal, technical, ethical, or safety issues without ...
Caner Korkmaz, Halil Eralp Kocas, Ahmet Uysal, Ahmed Masry, Oznur Ozkasap, Baris Akgun   +5 more
openaire   +1 more source

Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures

International Journal of Scientific Research in Computer Science Engineering and Information Technology
Federated learning (FL) on edge devices has emerged as a promising approach for decentralized model training, enabling data privacy and efficiency in distributed networks.
Lawrence Anebi Enyejo, Michael Babatunde Adewoye, Uchenna Nneka Ugochukwu   +2 more
semanticscholar   +1 more source

Federated learning (FL)-driven real-time decision support for intraoperative cardiovascular surgery: A privacy-preserving AI framework

Innovation and Emerging Technologies
Intraoperative cardiovascular surgery demands fast and precise decision-making utilizing patient data in real time. Unfortunately, centralized artificial intelligence (AI) models have inherent drawbacks, including high latency, scalability such as low ...
R. Kanthavel, R. Dhaya
semanticscholar   +1 more source

A Federated Learning (FL) Platform to Train the Machine Learning Model: A Step Towards Making FL More Efficient

Cureus Journal of Computer Science
Federated learning is an emerging technology that can revolutionize the training of machine learning models. Federated learning refers to an approach to training a machine learning model in a decentralized and collaborative fashion.
Gayatri M. Bhandari, Nitin M. Shivale, Shrishail Patil, P. Prajapati, Atharv Burujpatte, Diya Gandhi, Mangal Kasare   +6 more
semanticscholar   +1 more source

A survey on security and privacy of federated learning

Future generations computer systems, 2021
Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon decentralized data and training that brings learning to the edge or directly on-device.
Viraaji Mothukuri, R. Parizi, Seyedamin Pouriyeh, Yan-ping Huang, A. Dehghantanha, Gautam Srivastava   +5 more
semanticscholar   +1 more source