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FedCD: Improving Performance in non-IID Federated Learning
Federated learning has been widely applied to enable decentralized devices, which each have their own local data, to learn a shared model. However, learning from real-world data... -
FedMSRW: Federated MS Lesion Segmentation via Dynamic Re-Weighting Mechanisms
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning -
CIFAR10, CINIC10, SVHN, MNIST, FashionMNIST
The dataset used in the paper is a benchmark dataset for decentralized learning, consisting of 20 clients with different models and data distributions. -
Federated Learning-based Data Sharing Framework for Vehicular Networks
A federated learning-based data sharing framework for vehicular networks with FL and blockchain. -
Federated Learning-based Data Sharing Framework for Industrial IoT
A federated learning-based data sharing framework for industrial IoT with FL and blockchain. -
Federated Learning-based Data Sharing Framework for Internet of Vehicles
A federated learning-based data sharing framework for Internet of Vehicles (IoV) where each vehicle acts as an FL client to cooperatively share data with an aggregator (e.g., a... -
Ed-Fed: A generic federated learning framework with resource-aware client sel...
Ed-Fed is a comprehensive and generic FL framework for edge devices with resource-aware client selection for edge devices. -
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based ...
The dataset used in the paper is not explicitly described, but it is mentioned that the authors designed an incentive mechanism for joint resource allocation in blockchain-based... -
Hyperspectral pasture image dataset
Hyperspectral pasture image dataset with imbalanced class distributions and disparate volumes of data among different sites -
Federated Data Model
Medical image segmentation task using cardiac magnetic resonance images from different hospitals. -
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and... -
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via...
The authors propose a general framework to solve the two challenges simultaneously: inter-client data heterogeneity and intra-client data noise. The framework uses... -
Federated Unlearning via Class-Discriminative Pruning
We explore the problem of selectively forgetting categories from trained CNN classification models in federated learning (FL). -
Delay Sensitive Hierarchical Federated Learning
The authors studied the impact of local averaging on the performance of federated learning systems in the presence of communication delay between the clients and the parameter... -
Ransomware Detection using Federated Learning based on CNN Model
The dataset is used for ransomware detection using a CNN model based on federated learning. -
MNIST and CIFAR-10 datasets
The MNIST and CIFAR-10 datasets are used to test the theory suggesting the existence of many saddle points in high-dimensional functions. -
On-demand Quantization for Green Federated Diffusion in Mobile Edge Networks
The dataset used in this paper is a federated diffusion model for mobile edge networks. -
FedNST: Federated Noisy Student Training for Automatic Speech Recognition
Federated Noisy Student Training for Automatic Speech Recognition -
An On-Device Federated Learning Approach for Cooperative Model Update between...
The proposed on-device federated learning approach is evaluated using three datasets: UAH-DriveSet, Smartphone HAR, and MNIST.
