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DSA dataset
The dataset used in the paper is a Dynamic Spectrum Access (DSA) dataset, which is a special application in which Federated Learning (FL) can be applied for superior performance. -
Federated cINN Clustering Algorithm
Federated cINN Clustering Algorithm (FCCA) uses MNIST, FMNIST, Cifar10, Cifar100 and Synthetic datasets for experiments. -
Federated Learning for Cross-block Oil-water Layer Identification
Cross-block oil-water layer identification using Federated Learning for Cross-block Oil-water Layer Identification -
FEDEBA+: TOWARDS FAIR AND EFFECTIVE FEDERATED LEARNING VIA ENTROPY-BASED MODEL
Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to perform consistently across all clients. However, designing an FL algorithm that... -
Tiny-ImageNet, CIFAR100, and CIFAR10
The authors used Tiny-ImageNet, CIFAR100, and CIFAR10 datasets for their experiments. -
COMPAS, Adult, Law School, Dutch Census
The COMPAS dataset’s prediction task is to calculate the recidivism outcome, indicating whether individuals will be rearrested within two years after the first arrest, with race... -
One-shot Federated Learning without Server-side Training
Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection. The authors propose a novel model aggregation method... -
Scaffold: Stochastic Controlled Averaging for Federated Learning
Federated learning has emerged as an important paradigm in modern large-scale machine learning. Unlike in traditional centralized learning where models are trained using large... -
Human Brain Cell Type Classification data set
The dataset used for the experiments with federated learning on transcriptomic data. -
Acute Myeloid Leukemia data set
The dataset used for the experiments with federated learning on transcriptomic data. -
Purchase100
Purchase dataset contains 600 different products (attributes), and each user has a binary record which indicates whether she has bought each of the products (a total of 197,324... -
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. -
Multi-Task Learning for Federated Classification and Regression
The proposed algorithm allows personalizing the learning model for each participant without sharing the training data and improves the performance, compared to that of the... -
A Fair Federated Learning Framework With Reinforcement Learning
Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally... -
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
This paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog... -
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual...
We study federated contextual linear bandits, where M agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We... -
Federated Learning-based Data Sharing Framework for Vehicular Networks
A federated learning-based data sharing framework for vehicular networks with FL and blockchain.
