Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data [PDF]
The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving the privacy of clients' data.
Morafah, Mahdi +3 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
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
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
Kernel-Weighted Aggregation for Hyperparameter-Free Quantum Federated Learning. [PDF]
Entropy (Basel)Huang R.
europepmc +1 more source
FedPome: federated deep learning for real-time pomegranate disease classification. [PDF]
Front Plant SciS L +5 more
europepmc +1 more source
Proxy-Based Diagnostics of Quantum Oracle Sketching Robustness for Non-IID Sensor and Telemetry Streams. [PDF]
Sensors (Basel)Farsi M, Mahmoud M, Helmy A.
europepmc +1 more source