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

Last update: Dec 23, 2021

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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

PyTorch implementation of hand mesh reconstruction described in CMR and MobRecon.

Author: Wenhao Yu ([email protected]). ACL 2022. Commonsense Reasoning on Knowledge Graph for Text Generation

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SeisComP/SeisBench interface to enable deep-learning (re)picking in SeisComP

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Plugin adapted from Ultralytics to bring YOLOv5 into Napari

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