Results 111 to 120 of about 33,062,051 (187)
Addressing Non-IID Data in Federated Learning with Dual Attention Mechanism for Edge Computing ...
A Yao (13306335) +5 more
core
Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively train ML models without sharing their private data.
Kourtellis, Nicolas +6 more
core +1 more source
FedPLC: Federated Learning with Dynamic Cluster Adaptation for Concept Drift on Non-IID Data. [PDF]
Sensors (Basel)Zhou Q +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 MedMuntaqim MDZ, Smrity TA.
europepmc +1 more source
FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning
FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated ...
X Fan (7674161) +4 more
core
Feature matching data synthesis for non-IID federated learning
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server.
Sun, Yuchang +5 more
core +1 more source
A Study of Enhancing Federated Learning on Non-IID Data with Server Learning. [PDF]
IEEE Trans Artif IntellMai VS, La RJ, Zhang T.
europepmc +1 more source
A Review of Solving Non-IID Data in Federated Learning: Current Status and Future Directions
Federated learning (FL), as a machine learning framework, has garnered substantial attention from researchers in recent years. FL makes it possible to train a global model through coordination by a central server while ensuring the privacy of data on ...
Xiulai Li (20258019) +3 more
core +1 more source