PyTorch implementation of federated learning framework based on the acceleration of global momentum

Last update: Dec 23, 2021

Related tags

Deep Learning FedAGM

Overview

Federated Learning with Acceleration of Global Momentum

PyTorch implementation of federated learning framework based on the acceleration of global momentum.

Federated Learning with Acceleration of Global Momentum

Geeho Kim, Jinkyu Kim, Bohyung Han

1. Dependencies

This repository is implemented based on PyTorch with Anaconda.

2. Training models

CIFAR-100, 100 clients, Dirichlet (0.3) split, 5% participation

python federated_train.py --cuda_visible_device=1 --method=FedAGM --global_method=FedAGM --config=configs/cifar_actl2.yaml --arch=ResNet18 --weight_decay=1e-3 --gr_clipping_max_norm=10 --alpha=1 --mu=0.001 --gamma=0.9 --momentum=0.0 --tau 1.0 --lr=1e-1 --mode=dirichlet --dirichlet_alpha=0.3  --participation_rate=0.05 --learning_rate_decay 0.995 --set CIFAR100

Owner

GitHub Repository

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PyTorch implementation of federated learning framework based on the acceleration of global momentum

Related tags

Overview

Federated Learning with Acceleration of Global Momentum

1. Dependencies

2. Training models

Owner

traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation toolbox based on PyTorch.

Steer OpenAI's Jukebox with Music Taggers

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.

Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

Pytorch port of Google Research's LEAF Audio paper

Pytorch Implementation of Various Point Transformers

NFT-Price-Prediction-CNN - Using visual feature extraction, prices of NFTs are predicted via CNN (Alexnet and Resnet) architectures.

Specification language for generating Generalized Linear Models (with or without mixed effects) from conceptual models

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This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

Learned image compression

Facilitating Database Tuning with Hyper-ParameterOptimization: A Comprehensive Experimental Evaluation

Library for time-series-forecasting-as-a-service.

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