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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. -
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... -
Sent140 dataset
The dataset used in the paper is a real-world dataset for sentiment analysis. -
FEMNIST dataset
Mobile crowdsensing has gained significant attention in recent years and has become a critical paradigm for emerging Internet of Things applications. The sensing devices...
