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.
| Variable Name | Default Value | Optional Values | Description |
|---|---|---|---|
| --seed | 0 | integer | Seed for client selection and batch splitting. |
| --metrics-name | "metrics" | string | Name for metrics file. |
| --metrics-dir | "metrics" | string | Folder name for metrics files. |
| --log-dir | "logs" | string | Folder name for log files. |
| --use-val-set | None | None | Set this option to use the validation set, otherwise the test set is used. (NOT TESTED) |
Custom Hyperparameters
These hyperparameters are included in scripts/run.sh. We list them below.
| Environment Variable | Default Value | Description |
|---|---|---|
| CONTAINER_RANK | 0 | This identify the container (e.g., fedgs.0) and log files (e.g., logs/femnist/0/output.0). |
| BATCH_SIZE | 32 | Number of training samples in each batch. |
| LEARNING_RATE | 0.01 | Learning rate for local optimizers. |
| NUM_GROUPS | 10 | Number of groups. |
| CLIENTS_PER_GROUP | 10 | Number of clients selected in each group. |
| SAMPLER | gbp-cs | Sampler to be used, can be random, brute, bayesian, probability, ga and gbp-cs. |
| NUM_SYNCS | 50 | Number of internal synchronizations in each round. |
| NUM_ROUNDS | 500 | Total rounds of external synchronizations. |
| DATASET | femnist | Dataset to be used, only FEMNIST is supported currently. |
| MODEL | cnn | Neural network model to be used. |
| EVAL_EVERY | 1 | Interval rounds for model evaluation. |
| NUM_GPU_AVAILABLE | 2 | Number of GPUs available. |
| NUM_GPU_BEGIN | 0 | Index of the first available GPU. |
| IMAGE_NAME | fedgs | Experimental image to be used. |
NOTE: If you wish to specify a GPU device (e.g., GPU0), please set
NUM_GPU_AVAILABLE=1andNUM_GPU_BEGIN=0.
NOTE: This script will mount project files
/home/lizh/fedgsfrom 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]
487 Dec 27, 2022
81 Dec 22, 2022
78 Dec 08, 2022
920 Jan 04, 2023
122 Dec 28, 2022
21k Jan 06, 2023
148 Dec 28, 2022
206 Dec 20, 2022
1 Feb 14, 2022
40 Dec 06, 2022
229 Oct 21, 2022
5 May 06, 2022
246 Dec 19, 2022
758 Dec 25, 2022
124 Jan 07, 2023
0 Dec 24, 2021
46.9k Jan 03, 2023
207 Jan 05, 2023
47 Sep 20, 2022
2 Jan 05, 2022