Results 81 to 90 of about 12,973 (156)

Split Averaging: Bridging the Heterogeneity Gap in Clients Data for Federated Learning

IEEE Access
Federated Learning (FL) has gained significant prominence to overcome the issue of data silos in various domains. However, since its introduction FL has been confronted with the presence of Non-Independent and Identically Distributed (Non-IID) data ...
Sajjad Khan, Nikita Karetnikov, Muhammad Habib Ur Rehman, Davor Svetinovic   +3 more
doaj   +1 more source

Enhancing federated learning for IoT-based anomaly detection: A reputation-based client selection approach

Alexandria Engineering Journal
Federated Learning (FL) enables collaborative model training across decentralized, privacy-sensitive environments but often suffers from slow convergence, unbalanced client selection, and non‑IID data challenges.
Maha Jawad Alfadhil, Ali Baydoun, Moutaz Alazab, Hafeez Ur Rehman, Jihad Al Jaam, Sumaya Ali S.A. Al-Maadeed   +5 more
doaj   +1 more source

Chromatic PAC-Bayes Bounds for Non-IID Data.

, 2009
Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned with \iid data, and it is particularly so for margin classifiers. However, there are many practical cases where the training data show some dependencies and where the traditional \iid assumption does not apply.
Ralaivola, Liva, Szafranski, Marie, Stempfel, Guillaume   +2 more
openaire   +2 more sources

An Overview of Autonomous Connection Establishment Methods in Peer-to-Peer Deep Learning

IEEE Access
The exchange of model parameters between peers is critical in peer-to-peer deep learning. Historically, connections between agents were assigned randomly based on network topology.
Robert Sajina, Nikola Tankovic, Ivo Ipsic   +2 more
doaj   +1 more source

FedPLC: Federated Learning with Dynamic Cluster Adaptation for Concept Drift on Non-IID Data. [PDF]

Sensors (Basel)
Zhou Q, Yu Y, Ma J, Obaidat MS, Chang X, Ma M, Sun S.   +6 more
europepmc   +1 more source

Federated Learning for Human Pose Estimation on Non-IID Data via Gradient Coordination. [PDF]

Sensors (Basel)
Ni P, Xiang D, Jiang D, Sun J, Cui J.
europepmc   +1 more source

Federated Learning Framework for Brain Tumor Detection Using MRI Images in Non-IID Data Distributions. [PDF]

J Imaging Inform Med
Muntaqim MDZ, Smrity TA.
europepmc   +1 more source

A Study of Enhancing Federated Learning on Non-IID Data with Server Learning. [PDF]

IEEE Trans Artif Intell
Mai VS, La RJ, Zhang T.
europepmc   +1 more source

Adaptive Multi-Model Hierarchical Federated Learning for Robust IoT Intrusion Detection. [PDF]

Sensors (Basel)
Latif S, Djenouri D.
europepmc   +1 more source