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
GitHub Repository
Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019

USSS_ICCV19 Code for Universal Semi Supervised Semantic Segmentation accepted to ICCV 2019. Full Paper available at https://arxiv.org/abs/1811.10323.

Tarun K 68 Nov 24, 2022
Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Demonstration of OpenVINO techniques - Model-division and a simplest-way to support custom layers Description: Model Optimizer in Intel(r) OpenVINO(tm

Yasunori Shimura 12 Nov 09, 2022
Segmentation models with pretrained backbones. PyTorch.

Python library with Neural Networks for Image Segmentation based on PyTorch. The main features of this library are: High level API (just two lines to

Pavel Yakubovskiy 6.6k Jan 06, 2023
MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL [ Documentation | Demo Video ] MetaDrive is a driving simulator with the following

DeciForce: Crossroads of Machine Perception and Autonomy 276 Jan 04, 2023
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

Self-Supervised Vision Transformers with DINO PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supe

Facebook Research 4.2k Jan 03, 2023
Tracking Progress in Question Answering over Knowledge Graphs

Tracking Progress in Question Answering over Knowledge Graphs Table of contents Question Answering Systems with Descriptions The QA Systems Table cont

Knowledge Graph Question Answering 47 Jan 02, 2023
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

Rithesh Kumar 135 Oct 27, 2022
Code for the paper "Zero-shot Natural Language Video Localization" (ICCV2021, Oral).

Zero-shot Natural Language Video Localization (ZSNLVL) by Pseudo-Supervised Video Localization (PSVL) This repository is for Zero-shot Natural Languag

Computer Vision Lab. @ GIST 37 Dec 27, 2022
How to Predict Stock Prices Easily Demo

How-to-Predict-Stock-Prices-Easily-Demo How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube ##Overview This is th

Siraj Raval 752 Nov 16, 2022
Wider-Yolo Kütüphanesi ile Yüz Tespit Uygulamanı Yap

WIDER-YOLO : Yüz Tespit Uygulaması Yap Wider-Yolo Kütüphanesinin Kullanımı 1. Wider Face Veri Setini İndir Train Dataset Val Dataset Test Dataset Not:

Kadir Nar 6 Aug 22, 2022
A novel Engagement Detection with Multi-Task Training (ED-MTT) system

A novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes MSE and triplet loss together to determine the engagement level of students in an e-learning environment.

Onur Çopur 12 Nov 11, 2022
WTTE-RNN a framework for churn and time to event prediction

WTTE-RNN Weibull Time To Event Recurrent Neural Network A less hacky machine-learning framework for churn- and time to event prediction. Forecasting p

Egil Martinsson 727 Dec 28, 2022
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

causal-bald | Abstract | Installation | Example | Citation | Reproducing Results DUE An implementation of the methods presented in Causal-BALD: Deep B

OATML 13 Oct 07, 2022
Structured Edge Detection Toolbox

################################################################### # # # Structure

Piotr Dollar 779 Jan 02, 2023
A novel benchmark dataset for Monocular Layout prediction

AutoLay AutoLay: Benchmarking Monocular Layout Estimation Kaustubh Mani, N. Sai Shankar, J. Krishna Murthy, and K. Madhava Krishna Abstract In this pa

Kaustubh Mani 39 Apr 26, 2022
TeachMyAgent is a testbed platform for Automatic Curriculum Learning methods in Deep RL.

TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL Paper Website Documentation TeachMyAgent is a testbed platform for Automatic Cu

Flowers Team 51 Dec 25, 2022
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

167 Jan 02, 2023
Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence

Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. This article aims to provide an introduction on how to make use of the S

RISHABH MISHRA 1 Feb 13, 2022
This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations at CVPR'21. According to some product reasons, we are not planning to release the training/testing codes and models. However, we will release the dataset and the scripts to prepare the dataset.

TransFill-Reference-Inpainting This is the official repo for TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transf

Yuqian Zhou 80 Dec 08, 2022