Results 21 to 30 of about 33,062,051 (187)

Entropy-Regularized Federated Optimization for Non-IID Data

Algorithms
Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance.
Koffka Khan
doaj   +2 more sources

Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment

IEEE Access, 2023
Machine Learning (ML) algorithms process input data making it possible to recognize and extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide knowledge in a Federated Learning (FL) environment, sharing parameters ...
Fernanda C. Orlandi, Julio C. S. Dos Anjos, Valderi R. Q. Leithardt, Juan Francisco De Paz Santana, Claudio F. R. Geyer   +4 more
doaj   +2 more sources

ProFed: A Benchmark for Proximity-Based Non-IID Federated Learning

Journal of Open Research Software
Federated Learning (FL) has emerged as a key paradigm in machine learning but its performance often deteriorates under non-independent and identically distributed (non-IID) client data.
Davide Domini, Christian Otte Ingemann, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli   +4 more
doaj   +2 more sources

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Engineering, 2016
While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services.
Longbing Cao
exaly   +3 more sources

Federated multimodal malware classification under non-IID data

Cybersecurity
Malware data in real-world cybersecurity applications are typically distributed across multiple organizations, and privacy, security, and compliance constraints prevent these data from being shared with a central server.
Shaohua Liu, Wenbo Zhang, Tingting Yang, Guanghao Li, Lihua Zhang   +4 more
doaj   +2 more sources

Advanced Optimization Techniques for Federated Learning on Non-IID Data

Future Internet
Federated learning enables model training on multiple clients locally, without the need to transfer their data to a central server, thus ensuring data privacy.
Filippos Efthymiadis, Aristeidis Karras, Christos Karras, Spyros Sioutas   +3 more
doaj   +2 more sources

FedProc: Prototypical Contrastive Federated Learning on Non-IID Data

Future Generation Computer Systems, 2023
Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of all clients are not independent and identically distributed (i.e., non-IID), it is challenging to implement this form of efficient collaborative learning.
Ke Cheng, Yulong Shen, Xutong Mu
exaly   +3 more sources

Federated Learning Architecture for Non-IID Data [PDF]

Jisuanji gongcheng, 2023
In the scenarios of federated learning involving ultra-large-scale edge devices, the local data of participants are non-Independent Identically Distribution(non-IID) pattern, resulting in an imbalance in overall training data and difficulty in defending ...
Tianchen QIU, Xiaoying ZHENG, Yongxin ZHU, Songlin FENG
doaj   +1 more source

Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks [PDF]

Scientific Reports
Multi-institutional healthcare Internet of Things (IoT) networks face a core challenge between combined intrusion detection and patient data privacy.
Sudhakar Sengan, Chin-Shiuh Shieh
doaj   +2 more sources

A Multiscale Clustering Approach for Non‐IID Nominal Data [PDF]

Computational Intelligence and Neuroscience, 2021
Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent ...
Runzi Chen, Shuliang Zhao, Zhenzhen Tian
openaire   +3 more sources