# Real-World Image Datasets for Federated Learning

arXiv: https://arxiv.org/abs/1910.11089 · HTML: https://mlsanity.com/paper/1910.11089/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 30 of 30)

- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [Multi-Center Federated Learning: Clients Clustering for Better Personalization](https://mlsanity.com/paper/2108.08647/) (2021)
- [Divergence-aware Federated Self-Supervised Learning](https://mlsanity.com/paper/2204.04385/) (2022)
- [Collaborative Unsupervised Visual Representation Learning from Decentralized Data](https://mlsanity.com/paper/2108.06492/) (2021)
- [Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2206.02465/) (2022)
- [On the Convergence of Clustered Federated Learning](https://mlsanity.com/paper/2202.06187/) (2022)
- [Personalized Federated Learning With Graph](https://mlsanity.com/paper/2203.00829/) (2022)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [CalFAT: Calibrated Federated Adversarial Training with Label Skewness](https://mlsanity.com/paper/2205.14926/) (2022)
- [Decentralized Unsupervised Learning of Visual Representations](https://mlsanity.com/paper/2111.10763/) (2021)
- [FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems](https://mlsanity.com/paper/2304.01534/) (2023)
- [Auto-weighted Robust Federated Learning with Corrupted Data Sources](https://mlsanity.com/paper/2101.05880/) (2021)
- [Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning](https://mlsanity.com/paper/2105.07606/) (2021)
- [Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis](https://mlsanity.com/paper/2205.12144/) (2022)
- [FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning](https://mlsanity.com/paper/2208.02442/) (2022)
- [A Privacy Preserving System for Movie Recommendations Using Federated Learning](https://mlsanity.com/paper/2303.04689/) (2023)
- [FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler](https://mlsanity.com/paper/2405.15458/) (2024)
- [Federated Object Detection for Quality Inspection in Shared Production](https://mlsanity.com/paper/2306.17645/) (2023)
- [Comparison of Object Detection Algorithms for Street-level Objects](https://mlsanity.com/paper/2208.11315/) (2022)
- [NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data](https://mlsanity.com/paper/2306.15932/) (2023)
- [FeDETR: a Federated Approach for Stenosis Detection in Coronary Angiography](https://mlsanity.com/paper/2409.14268/) (2024)
- [Federated Learning for Commercial Image Sources](https://mlsanity.com/paper/2507.12903/) (2025)
- [Mobile Application for Oral Disease Detection using Federated Learning](https://mlsanity.com/paper/2403.12044/) (2023)
- [Federated Learning with Limited Node Labels](https://mlsanity.com/paper/2406.12435/) (2024)

## Abstract (arXiv)

Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private. While research efforts on federated learning have been growing tremendously in the past two years, most existing works still depend on pre-existing public datasets and artificial partitions to simulate data federations due to the lack of high-quality labeled data generated from real-world edge applications. Consequently, advances on benchmark and model evaluations for federated learning have been lagging behind. In this paper, we introduce a real-world image dataset. The dataset contains more than 900 images generated from 26 street cameras and 7 object categories annotated with detailed bounding box. The data distribution is non-IID and unbalanced, reflecting the characteristic real-world federated learning scenarios. Based on this dataset, we implemented two mainstream object detection algorithms (YOLO and Faster R-CNN) and provided an extensive benchmark on model performance, efficiency, and communication in a federated learning setting. Both the dataset and algorithms are made publicly available.