Datasets from FedScale – 2021

Description: This repository contains scripts and instructions of building FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning. For Continue Reading »

Datasets From LEAF – 2018

Overview: LEAF is one of the earliest dataset proposals for federated learning. It contains six datasets covering different domains, including image classification, sentiment analysis and next-character prediction. A set of utilities is provided to divided datasets into different parties in an IID or non-IID way. FEMNIST dataset and Shakespeare dataset are selected since as opposed Continue Reading »

Datasets From FedML – 2020

Overview: FedML is a research library that provides both frameworks for federated learning and benchmark functionalities. As a benchmark, it provides comprehensive baseline implementations for multiple ML models and FL algorithms, including FedAvg, FedNAS, Vertical FL and split learning. Moreover, it supports three computing paradigms, namely distributed training, mobile on-device training, and standalone simulation.  Also, Continue Reading »