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45 changes: 45 additions & 0 deletions .gitignore
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*.tar.gz
*.zip
*.h5
*.pkl
/log
*.e
*.o
/dist
*egg-info*/
/build
__py*/
eval_data/
.vscode/

*.swp
.DS_Store
*swo
learner*.lsf
server*.lsf
*swn
*swp
*save*
*pyc
*log
*pys
*.o
*.e
blog_data_dist.txt
SampleResults
MultiRs*
# logs
# logs_bk
bk
openImg_size.txt
openImgFull_size.txt
pymp*
learn*.lsf
server*.lsf
vms
jobinfo
./scripts/logs/*
*.pth.tar
clientInfoFile
*.sb

202 changes: 202 additions & 0 deletions LICENSE
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149 changes: 149 additions & 0 deletions README.md
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# PyramidFL

This repository contains scripts and instructions for reproducing the experiments in our MobiCom'22 paper "
PyramidFL: A Fine-grained Participant Selection Framework forEfficient Federated Learning".
<!-- [PyramidFL: A Fine-grained Participant Selection Framework forEfficient Federated Learning](https://www.usenix.org/conference/osdi21/presentation/lai)". -->

# Overview

* [Environment Setting](#environment-setting)
* [Repo Structure](#repo-structure)
* [Reproduce Results with Provided Logs](#reproduce-results-with-provided-logs)
* [Reproduce Results from Scratch](#reproduce-results-from-scratch)
* [Acknowledgements](#acknowledgements)
* [Contact](#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.

``` python
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)

```

``` matlab
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](https://github.com/SymbioticLab/FedScale). And HARBox can be found on [FL-Datasets-for-HAR](https://github.com/xmouyang/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`.

``` bash
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.
```bibtex
@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 and Xiangfeng Zhu for their OSDI'21 paper [Oort: Efficient Federated Learning via Guided Participant Selection](https://www.usenix.org/conference/osdi21/presentation/lai). The source codes can be found in repo [Oort](https://github.com/SymbioticLab/Oort).

We also appreciate the help from Xiaomin Ouyang with their MobiSys'21 paper [ClusterFL: a similarity-aware federated learning system for human activity recognition](https://dl.acm.org/doi/10.1145/3458864.3467681). The HARBox dataset can be found in repo [FL-Datasets-for-HAR](https://github.com/xmouyang/FL-Datasets-for-HAR).


# Contact
Chenning Li by [email protected]


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