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FedScale Benchmarking Datasets

Realistic FL evaluation requires careful dataset selection and environment setup. As such, FedScale provides two categories of datasets:

  1. Workload datasets that represent diverse FL tasks; and
  2. Environment datasets that reflect settings FL will be deployed.

When running in the benchmark evaluation mode, FedScale runtime can leverage the latter to evaluate the former in realistic settings.

Download

You can run fedscale dataset (or use download.sh) to download or remove individual datasets.

# Run `fedscale dataset help` for more details
`fedscale dataset download [dataset_name]`   # Or `bash download.sh download [dataset_name] `

Workload Datasets Details

We are continuously adding more datasets! Please contribute.

We provide real-world datasets for the federated learning community, and plan to release much more soon! Each one is associated with its training, validation and testing dataset. A summary of statistics for training datasets can be found in the table below, and you can refer to each folder for more details.

You can use this example code to explore any of the FedScale datasets.

CV Tasks

Dataset Data Type # of Clients # of Samples Example Task
iNature Image 2,295 193K Classification
FMNIST Image 3,400 640K Classification
OpenImage Image 13,771 1.3M Classification, Object detection
Google Landmark Image 43,484 3.6M Classification
Charades Video 266 10K Action recognition
VLOG Video 4,900 9.6k Video classification, Object detection
Waymo Motion Video 496,358 32.5M Motion prediction

NLP Tasks

Dataset Data Type # of Clients # of Samples Example Task
Europarl Text 27,835 1.2M Text translation
Blog Corpus Text 19,320 137M Word prediction
Stackoverflow Text 342,477 135M Word prediction, classification
Reddit Text 1,660,820 351M Word prediction
Amazon Review Text 1,822,925 166M Classification, Word prediction
CoQA Text 7,685 116K Question Answering
LibriTTS Text 2,456 37K Text to speech
Google Speech Audio 2,618 105K Speech recognition
Common Voice Audio 12,976 1.1M Speech recognition

Misc Applications

Dataset Data Type # of Clients # of Samples Example Task
Taxi Trajectory Text 442 1.7M Sequence Prediction
Puffer Text 121,551 15.4M Sequence Prediction
Taobao Text 182,806 20.9M Recommendation
Go dataset Text 150,333 4.9M Reinforcement learning

Note that no details were kept of any of the participants age, gender, or location, and random ids were assigned to each individual. In using these datasets, we will strictly obey to their licenses, and these datasets provided in this repo should be used for research purpose only.

Examples

Google Speech Commands

A speech recognition dataset with over ten thousand clips of one-second-long duration. Each clip contains one of the 35 common words (e.g., digits zero to nine, "Yes", "No", "Up", "Down") spoken by thousands of different people.

OpenImage

OpenImage is a vision dataset collected from Flickr, an image and video hosting service. It contains a total of 16M bounding boxes for 600 object classes (e.g., Microwave oven). We clean up the dataset according to the provided indices of clients.

Reddit and StackOverflow

Reddit (StackOverflow) consists of comments from the Reddit (StackOverflow) website. It has been widely used for language modeling tasks, and we consider each user as a client. In our benchmark, we restrict to the 30k most frequently used words, and represent each sentence as a sequence of indices corresponding to these 30k frequently used words. We use Transformers to tokenize these sequences with a block size 64.

Repo Structure

.
|---- data          # Dictionary of each datasets 
|---- donwload.sh   # Download tool of each dataset

Envioronment Datasets

One of the key challenges in FL is reproducing the environment where FL will likely be deployed. To this end, we provide environmental datasets to reproduce heterogeneous device performance and device availability traces.

Heterogeneous System Performance

We use the AIBench dataset and MobiPerf dataset. AIBench dataset provides the computation capacity of different models across a wide range of devices. As specified in real FL deployments, we focus on the capability of mobile devices that have > 2GB RAM in this benchmark. To understand the network capacity of these devices, we clean up the MobiPerf dataset, and provide the available bandwidth when they are connected with WiFi, which is preferred in FL as well.

Availability of Clients

We use a large-scale real-world user behavior dataset from FLASH. It comes from a popular input method app (IMA) that can be downloaded from Google Play, and covers 136k users and spans one week from January 31st to February 6th in 2020. This dataset includes 180 million trace items (e.g., battery charge or screen lock) and we consider user devices that are in charging to be available, as specified in real FL deployments.

References

Please read and/or cite as appropriate to use FedScale code or data or learn more about FedScale.

@inproceedings{fedscale-icml22,
  title={{FedScale}: Benchmarking Model and System Performance of Federated Learning at Scale},
  author={Fan Lai and Yinwei Dai and Sanjay S. Singapuram and Jiachen Liu and Xiangfeng Zhu and Harsha V. Madhyastha and Mosharaf Chowdhury},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2022}
}

and

@inproceedings{oort-osdi21,
  title={Oort: Efficient Federated Learning via Guided Participant Selection},
  author={Fan Lai and Xiangfeng Zhu and Harsha V. Madhyastha and Mosharaf Chowdhury},
  booktitle={USENIX Symposium on Operating Systems Design and Implementation (OSDI)},
  year={2021}
}