FedGS: A Federated Group Synchronization Framework Implemented by LEAF-MX.

Related tags

Deep Learningfederated-learning
Overview

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=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]

Owner
Lizonghang
Intelligent Communication System, Distributed Machine Learning, Federated Learning
Lizonghang
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