Results 21 to 30 of about 4,684 (203)

A Bibliometric Analysis on Federated Learning

Journal of Advanced Research in Natural and Applied Sciences
With the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community.
Ömer Algorabi, Mesut Ulu, Yusuf Sait Türkan, Ersin Namlı   +3 more
doaj   +1 more source

Towards Efficient Federated Learning: Layer-Wise Pruning-Quantization Scheme and Coding Design

Entropy, 2023
As a promising distributed learning paradigm, federated learning (FL) faces the challenge of communication–computation bottlenecks in practical deployments. In this work, we mainly focus on the pruning, quantization, and coding of FL. By adopting a layer-
Zheqi Zhu, Yuchen Shi, Gangtao Xin, Chenghui Peng, Pingyi Fan, Khaled B. Letaief   +5 more
doaj   +1 more source

MarS-FL: Enabling Competitors to Collaborate in Federated Learning

IEEE Transactions on Big Data
Federated learning (FL) is rapidly gaining popularity and enables multiple data owners ({\em a.k.a.} FL participants) to collaboratively train machine learning models in a privacy-preserving way. A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness.
Xiaohu Wu, Han Yu 0001
openaire   +3 more sources

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source

Adapting security and decentralized knowledge enhancement in federated learning using blockchain technology: literature review

Journal of Big Data
Federated Learning (FL) is a promising form of distributed machine learning that preserves privacy by training models locally without sharing raw data.
Menna Mamdouh Orabi, Osama Emam, Hanan Fahmy   +2 more
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Advancing Federated Learning: A Systematic Literature Review of Methods, Challenges, and Applications

IEEE Access
Federated Learning (FL) has emerged as a cutting-edge paradigm in machine learning, showcasing remarkable advancements in recent years. This research paper delves into the dynamic landscape of FL by addressing four pivotal research questions.
Tamanna Zubairi Sana, Shahab Abdulla, Anindya Nag, Ayontika Das, Md. Mehedi Hassan, Zoya Zubairi Fiza, Asif Karim, Sheikh Ridwan Raihan Kabir   +7 more
doaj   +1 more source

PLDP-FL: Federated Learning with Personalized Local Differential Privacy

Entropy, 2023
As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning schemes based on local differential privacy (LDP) ignore an important issue: they do not ...
Xiaoying Shen, Hang Jiang, Yange Chen, Baocang Wang, Le Gao   +4 more
openaire   +3 more sources

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

ABC-FL: Anomalous and Benign client Classification in Federated Learning

CoRR, 2021
Federated Learning is a distributed machine learning framework designed for data privacy preservation i.e., local data remain private throughout the entire training and testing procedure. Federated Learning is gaining popularity because it allows one to use machine learning techniques while preserving privacy.
Hyejun Jeong, Joonyong Hwang, Tai-Myung Chung   +2 more
openaire   +2 more sources