FedGS: Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

Preparation

• For instructions on generating data, please go to the folder of the corresponding dataset. For FEMNIST, please refer to femnist.

• NVIDIA-Docker is required.

• NVIDIA CUDA version 10.1 and higher is required.

How to run FedGS

Build a docker image

Enter the scripts folder and build a docker image named fedgs.

sudo docker build -f build-env.dockerfile -t fedgs .

Modify /home/lizh/fedgs to your actual project path in scripts/run.sh. Then run scripts/run.sh, which will create a container named fedgs.0 if CONTAINER_RANK is set to 0 and starts the task.

chmod a+x run.sh && ./run.sh

The output logs and models will be stored in a logs folder created automatically. For example, outputs of the FEMNIST task with container rank 0 will be stored in logs/femnist/0/.

Hyperparameters

We categorize hyperparameters into default settings and custom settings, and we will introduce them separately.

Default Hyperparameters

These hyperparameters are included in utils/args.py. We list them in the table below (except for custom hyperparameters), but in general, we do not need to pay attention to them.

Custom Hyperparameters

These hyperparameters are included in scripts/run.sh. We list them below.

NOTE: If you wish to specify a GPU device (e.g., GPU0), please set NUM_GPU_AVAILABLE=1 and NUM_GPU_BEGIN=0.

NOTE: This script will mount project files /home/lizh/fedgs from the host into the container /root, so please check carefully whether your file path is correct.

Visualization

The visualizer metrics/visualize.py reads metrics logs (e.g., metrics/metrics_stat_0.csv and metrics/metrics_sys_0.csv) and draws curves of accuracy, loss and so on.

Reference

• This demo is implemented on LEAF-MX, which is a MXNET implementation of the well-known federated learning framework LEAF.

• Li, Zonghang, Yihong He, Hongfang Yu, et al. "Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT." Submitted to IEEE Internet of Things Journal, (2021).

• If you get trouble using this repository, please kindly contact us. Our email: [email protected]