HeteroSwitch Dataset

HeteroSwitch Dataset


HeteroSwitch Dataset


HeteroSwitch Dataset

Use Case

Computer Vision


Explore the HeteroSwitch Dataset, designed for research on system-induced data heterogeneity in Federated Learning environments.

HeteroSwitch Dataset

Research Context

The HeteroSwitch research, detailed in “HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning,” explores the challenges posed by data heterogeneity in Federated Learning (FL) systems. This research has been published on arXiv and presented at the 7th Annual Conference on Machine Learning and Systems (MLSys 2024), providing key insights into managing data inconsistencies across diverse systems.

Dataset Description

The HeteroSwitch dataset is specifically designed to support studies on system-induced data heterogeneity within Federated Learning frameworks. It includes a wide array of data collected from multiple types of devices, focusing on the effects of system variability on data uniformity.

Data Collection Methodology

To assess the influence of system-induced heterogeneity on FL, the dataset was generated using nine different smartphone models: Galaxy S22, Galaxy S9, Galaxy S6, VELVET, G7, G4, Pixel 5, Pixel 2, and Nexus 5X. Each smartphone was mounted on a tripod and used to capture images displayed on a monitor within a controlled darkroom setting. This method helps to minimize external variables such as lighting and positioning, ensuring that the primary source of data variation is the device itself.

Dataset Contents

The dataset consists of images from 12 distinct ImageNet classes, each representing different higher-level categories. The selected classes include Chihuahua, Altar, Cock, Abaya, Ambulance, Loggerhead, Timber Wolf, Tiger Beetle, Accordion, French Loaf, Barber Chair, and Orangutan. These classes were chosen to avoid overlap and to represent a broad spectrum of visual features.


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