etesami/fedscale

FedScale: Benchmarking Model and System Performance of Federated Learning

FedScale: Benchmarking Model and System Performance of Federated Learning (Paper)

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 each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at scale, we have also built an efficient evaluation platform, FedScale Automated Runtime (FAR), to simplify and standardize the process of FL experimental setup and model evaluation. Our evaluation platform provides flexible APIs to implement new FL algorithms and include new execution backends with minimal developer efforts.

FedScale is open-source with permissive licenses and actively maintained, and we welcome feedback and contributions from the community! If you have any questions or comments, please join our Slack channel.

Overview

• Getting Started
• Realistic FL Datasets
• Run Experiments with FAR
• Repo Structure
• Note
• Contact

Getting Started

Our install.sh will install the following automatically:

• Anaconda Package Manager
• CUDA 10.2

Note: if you prefer different versions of conda and CUDA, please check comments in install.sh for details.

Run the following commands to install FedScale.

git clone https://github.com/SymbioticLab/FedScale
cd FedScale
source install.sh 

Realistic FL Datasets

We are adding more datasets! Please feel free to contribute!

We provide real-world datasets for the federated learning community, and plan to release much more soon! Each is associated with its training, validation and testing dataset. A summary of statistics for training datasets can be found in Table, and you can refer to each folder for more details. Due to the super large scale of datasets, we are uploading these data and carefully validating their implementations to FAR. So we are actively making each dataset available for FAR experiments.

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

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	7,189	114K	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
Taobao	Text	182,806	0.9M	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.

Please go to ./dataset directory and follow the dataset README for more details.

Run Experiments with FAR

FedScale Automated Runtime (FAR), an automated and easily-deployable evaluation platform, to simplify and standardize the FL experimental setup and model evaluation under a practical setting. FAR is based on our Oort project, which has been shown to scale well and can emulate FL training of thousands of clients in each round.

Please go to ./core directory and follow the FAR README to set up FL training scripts.

Repo Structure

Repo Root
|---- dataset     # Realistic datasets in FedScale
|---- core        # Experiment platform of FedScale
    |---- examples  # Examples of new plugins
    |---- evals     # Backend of job submission
    

Notes

please consider to cite our paper if you use the code or data in your research project.

@inproceedings{fedscale-arxiv,
  title={FedScale: Benchmarking Model and System Performance of Federated Learning},
  author={Fan Lai and Yinwei Dai and Xiangfeng Zhu and Mosharaf Chowdhury},
  booktitle={arXiv:2105.11367},
  year={2021}
}

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}
}

Contact

Fan Lai ([email protected]), Yinwei Dai ([email protected]), Xiangfeng Zhu ([email protected]) and Mosharaf Chowdhury from the University of Michigan.