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Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

This is the code for paper "Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer".

Parameters

parameters description
rounds Number of rounds in training process, option:500
num_users Number of clients, option:40,20
local_bs Batch size for local training, option:5
beta Coefficient for local proximal term, option: 0.01,0
model neural network model, option: resnet18,resnet34,resnet50
dataset Dataset, option:cifar10,cifar100,imagenet and inat
iid Action iid or non iid, option: store_true
alpha_dirichlet Parameter for Dirichlet distribution, option: 10,1

Usage

  • To train on CIFAR-10 with IID data partition and imbalanced factor 100 over 40 clients:
python fed_grab.py --dataset cifar10 --iid --IF 0.01 --local_bs 5 --rounds 500 --num_users 40 --beta 0 --dataset cifar10  --model resnet18 --gpu 0
  • To train on CIFAR-10 with non-IID data partition with imbalanced factor 100 , alpha=1 over 40 clients:
python fed_grab.py --dataset cifar10 --alpha_dirichlet 1 --IF 0.01 --local_bs 5 --rounds 500 --num_users 40 --beta 0 --dataset cifar10  --model resnet18 --gpu 0