Simulation of Asynchronous Federated Learning

This is the repository for the work

Jin, Hongwei, Ning Yan, and Masood Mortazavi. "Simulating Aggregation Algorithms for Empirical Verification of Resilient and Adaptive Federated Learning." 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). IEEE, 2020.

Dataset generation

Data is preprocessed in the /tmp/data folder. More dataset will be preprocessed and store there. Please check the folder ./data for different dataset. Three datasets are used:

• mnist: dataset MNIST from OpenML
• nist: dataset NIST Special Dataset from NIST
• shakespeare: dataset Shakespeare plays original comes from Kaggle

In each subfolder under ./data, follow the instructions in README.md file to preprocess data. The preprocess data will be stored under /tmp/data folder. With the following file structure:

/tmp/data
|- mnist
   |- test
   |- train
|- nist
   |- test
   |- train
|- shakespeare
   |- test
   |- train

Federated Optimizer

• FedAvg: Communication-Efficient Learning of Deep Networks from Decentralized Data, AISTATS 2017

• FedProx: Federated Optimization for Heterogeneous Networks, MLSys 2020

• FedMom: proposed method to aggregation the global model by taking its previous trained model.

virtual environment

conda create -n fedsimul python=3
conda activate fedsimul
pip3 install -r requirements.txt  

Base case

• synchronous federated learning with all clients participated and refreshed

    python main.py --optimizer fedavg -p 1 -q 1
  

• asynchronous FL with fixed participate and refresh rate

    python main.py --asyn --optimizer fedavg 
  

• asynchronous FL with adaptive participate and adaptive refresh rate

•   python main.py --asyn --adp_p --adp_q --optimizer fedavg 
  

• asynchronous FL with adaptive participate and adaptive refresh rate with specified parcipate rate and refresh rate

    python main.py --asyn --adp_p --adp_q -p 0.02 -q 0.7 --optimizer fedavg
  

• asynchronous FL with specified window size

    python main.py --asyn --optimizer fedavg -w 5
  

• For more detailed arguments, please use

    usage: main.py [-h] [--optimizer {fedavg,fedprox,fedmom,fedmomprox}]
               [--dataset {mnist,nist,shakespeare}] [--model MODEL]
               [--num_rounds NUM_ROUNDS] [--eval_every EVAL_EVERY]
               [--batch_size BATCH_SIZE] [--num_epochs NUM_EPOCHS]
               [--learning_rate LEARNING_RATE] [--gamma GAMMA] [--seed SEED]
               [--gpu_id GPU_ID] [--verbose] [--asyn]
               [--participate_rate PARTICIPATE_RATE]
               [--refresh_rate REFRESH_RATE] [--adp_p] [--adp_q]
               [--window_size WINDOW_SIZE] [--alpha ALPHA]
  
    optional arguments:
    -h, --help            show this help message and exit
    --optimizer {fedavg,fedprox,fedmom,fedmomprox}
                          name of optimizer;
    --dataset {mnist,nist,shakespeare}
                          name of dataset;
    --model MODEL         name of model;
    --num_rounds NUM_ROUNDS
                          number of rounds to simulate;
    --eval_every EVAL_EVERY
                          evaluate every ____ rounds;
    --batch_size BATCH_SIZE
                          batch size when clients train on data;
    --num_epochs NUM_EPOCHS
                          number of epochs when clients train on data;
    --learning_rate LEARNING_RATE
                          learning rate for inner solver;
    --gamma GAMMA         constant for momentum
    --seed SEED           seed for randomness;
    --gpu_id GPU_ID       gpu_id
    --verbose             toggle the verbose output
    --asyn, -a            toggle asynchronous simulation
    --participate_rate PARTICIPATE_RATE, -p PARTICIPATE_RATE
                          probability of participating
    --refresh_rate REFRESH_RATE, -q REFRESH_RATE
                          probability of refreshing
    --adp_p               toggle adaptive participate rate
    --adp_q               toggle adaptive refresh rate
    --window_size WINDOW_SIZE, -w WINDOW_SIZE
                          moving window size
  

Example of running MNIST

Run the following instruction, replace $DATASET with a dataset of interest, specify the corresponding model to that dataset (choose from fedsimul/models/$DATASET/$MODEL.py and use $MODEL as the model name):

• with all the default arguments, one simple example is

python main.py

• example of running a asynchronous FL with adaptive participate rate and refresh rate.

python main.py --optimizer fedavg --model mclr --dataset mnist \
    --asyn --participate_rate 0.3 --refresh_rate 0.7 \
    --eval_every=1 --batch_size=10 \
    --num_epochs=20 --num_rounds=200

Setup using script

You can also setup the virtual environment by taking the setup.py script. Simply run it as development mode:

python setup.py develop

Plot the results

After running the simulation, resuls will be generated under ./out/{DATASET}. Each json file represent a different setting corresponding to the args in main.py.

Spepcify the files and metric want to be in the plot and the figure will be saved under ./out folder.

python plotfigure.py --help
usage: plotfigure.py [-h] [--metric {accuracy,flops}] [--outfile OUTFILE]
                     [--file FILE [FILE ...]] [--legend LEGEND [LEGEND ...]]

optional arguments:
  -h, --help            show this help message and exit
  --metric {accuracy,flops}
                        specify the metric
  --outfile OUTFILE     filename of saved png
  --file FILE [FILE ...]
                        files to plot
  --legend LEGEND [LEGEND ...]
                        legend of figure

reference

This repository is based on the work of

fedprox Federated Optimization for Heterogeneous Networks

MLSys 2020

License

The work is under MIT License