Description

This is the official code repository for the following paper accepted at TNNLS 2022:

Jiang, Y., Wang, S., Valls, V., Ko, B. J., Lee, W. H., Leung, K. K., & Tassiulas, L. (2022). Model pruning enables efficient federated learning on edge devices. IEEE Transactions on Neural Networks and Learning Systems.

This repository has some overlap with our model pruning library (https://github.com/jiangyuang/ModelPruningLibrary). However, this repository is for reproducing experiments from the paper only. We will not update this repository along with the model pruning library.

Setup

sudo -E python3 setup.py install

Run prototype

For each new terminal, please run

source setenv.sh

in the PruneFL root folder for the correct environment.

To run the prototype, first we need to update the configs/network.py configuration with appropriate address and port.

On the server side, run

# conventional FL
python3 experiments/FEMNIST/prototype/server.py -na -ni

for conventional FL, or run

# PruneFL
python3 experiments/FEMNIST/prototype/server.py -a -i

for PruneFL.

On each client side, always run

python3 experiments/FEMNIST/prototype/client.py

Run simulations

For each new terminal, please run

source setenv.sh

in the PruneFL root folder for the correct environment.

To auto-run all experiments, use

sh autorun/{experiment_name}.sh

to run all experiments for {experiment_name} (replace by the correct name).

We can also run single experiments using commands in the shell scripts.

Analyze results

Run

python3 analysis.py

to generate figures in results/{experiment_name}/figs folder for each experiment. Non-existing results will be skipped.

The code has been tested on Ubuntu 20.04, and example results are given in the example_results folder.