PyramidFL

This repository contains scripts and instructions for reproducing the experiments in our MobiCom'22 paper " PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning".

Overview

• Environment Setting
• Repo Structure
• Reproduce Results with Provided Logs
• Reproduce Results from Scratch
• Acknowledgements
• Contact

Environment Setting

Please run install.sh to 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 PyramidFL.

source install.sh 
git clone https://github.com/liecn/PyramidFL.git
cd PyramidFL

Repo Structure

Repo Root
+-- oort          # Oort code base.
+-- training
    +-- eval
        +-- configs             # Configuration for tasks
        +-- logs                # Performance logs for tasks
            +--google_speech 
            +--har
            +--openimage
            +--stackoverflow
            +--plot_{}.py       # plotting scripts
        +-- matlab              # Matlab scripts for fig 7-8
        +-- manager.py          # Main file for executing jobs
        +-- {task}_logging_{note}_{date}   # Raw logs
    +-- helper          # Client class and functions
    +-- utils       
    +-- learner.py      # Learner side for emulating clients
    +-- param_server.py # Central parameter server

Reproduce Results with Provided Logs

We provide the raw logs and performance records under the directory training/eval. To reproduce the figures in the paper directly, you can run following commands with corresponding python scripts.

cd training/eval/logs
python plot_perf_openimage_baseline_mobilenet.py.py     #Fig 2(a)
python plot_perf_openimage_baseline_shufflenet.py       #Fig 2(b)
python plot_perf_openimage_baseline_optimized_mobilenet.py          #Fig 6(a)
python plot_perf_openimage_baseline_optimized_shufflenet.py         #Fig 6(b)
python plot_perf_openimage_mobilenet_yogi.py            #Fig 10(a)
python plot_perf_openimage_mobilenet_prox.py            #Fig 10(b)
python plot_perf_openimage_shufflenet_yogi.py           #Fig 10(c)
python plot_perf_openimage_shufflenet_prox.py           #Fig 10(d)
python plot_perf_speech_yogi.py                         #Fig 11(a)
python plot_perf_speech_prox.py                         #Fig 11(b)
python plot_perf_stackoverflow_yogi.py                  #Fig 12(a)
python plot_perf_stackoverflow_prox.py                  #Fig 12(b)
python plot_perf_har_yogi.py                            #Fig 13(a)
python plot_perf_har_prox.py                            #Fig 13(b)

cd training/eval/matlab
matlab fig_client.m         #Fig 7(a)(b) and Fig 8(a)(b)

Note:

1. Generated figures can be found under current directory (e.g., logs, matlab).
2. Detailed raw logs can be found under the directory evals, according to the date. For example, in script evals/logs/plot_perf_speech_yogi.py, we use the performance records evals/logs/google_speech/0807_041041_28052/aggregator/training_perf. It corresponds to the raw log evals/google_speech_logging_random_resnet_yogi_0807_041041_28052.

Reproduce Results from Scratch

Please assure that all paths in the configurations are consistent for datasets, scripts, and logs.

1. Download datasets. Stackoverflow, Google Speech, and OpenImage can be found on FedScale. And HARBox can be found on FL-Datasets-for-HAR

We show the structure of our dataset directory as follows:

Dataset Root
+-- dataset
    +-- google_speech       # Speech recognition
        +-- _background_noise_
        +-- test 
        +-- train
        +-- clientDataMap
    +-- HARBox        # IMU-based human activity recognition
        +-- 0
        +-- ... 
        +-- 120 
    +-- open_images          # Image classification
        +-- test 
        +-- train
        +-- classTags
        +-- vocab_tags.txt
        +-- clientDataMap  
    +-- stackoverflow    # Next-work prediction
        +-- test 
        +-- train
        +-- albert-base-v2-config.json
        +-- vocab_tags.txt
        +-- vocab_tokens.txt  

2. Please run following commands to submit the task. For example, submit the configuration file evals/configs/speech/conf_random.yml to the main file manager.py.

cd {root}/PyramidFL/training/evals
python manager.py submit configs/speech/conf_random.yml

3. All logs will be dumped to log_path specified in the configuration file. training_perf locates at the master node under this path. Meanwhile, the user can check /evals/{task}_logging_{date} to see whether the job is moving on.

Notes

please cite our paper if you think the source codes are useful in your research project.

@inproceedings{PyramidFL_MobiCom22,
    author = {Li, Chenning and Zeng, Xiao and Zhang, Mi and Cao, Zhichao},
    title = {PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning},
    year = {2022},
    booktitle = {Proceedings of ACM MobiCom},
}

Acknowledgements

Thanks to Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury for their OSDI'21 paper Oort: Efficient Federated Learning via Guided Participant Selection. The source codes can be found in repo Oort.

We also appreciate the help from Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, and Guoliang Xing for their MobiSys'21 paper ClusterFL: a similarity-aware federated learning system for human activity recognition. The HARBox dataset can be found in repo FL-Datasets-for-HAR.

Contact

Chenning Li by [email protected]