use_gpu: True
device: 0
early_stop:
  patience: 0
seed: 2
federate:
  mode: standalone
  total_round_num: 20
  sample_client_num: 13
  merge_test_data: True
  share_local_model: False
  client_num: 200
  sampler: 'uniform'
  resource_info_file: 'client_device_capacity'
data:
  root: data/
  type: CIFAR10@torchvision
  args: [{'download': True}]
  splits: [0.8,0.2,0.2]
  subsample: 0.2
  transform: [['ToTensor'], ['Normalize', {'mean': [0.4914, 0.4822, 0.4465], 'std': [0.2470, 0.2435, 0.2616]}]]
  splitter: 'lda'
  splitter_args: [{'alpha': 0.2}]
dataloader:
  batch_size: 10
model:
  type: convnet2
  hidden: 512
  out_channels: 10
train:
  local_update_steps: 2
  batch_or_epoch: batch
  optimizer:
    lr: 0.5
    weight_decay: 0.0
grad:
  grad_clip: 5.0
criterion:
  type: CrossEntropyLoss
trainer:
  type: cvtrainer
eval:
  freq: 5
  metrics: ['acc', 'correct']
  best_res_update_round_wise_key: 'test_acc'
asyn:
  use: True
  overselection: False
  staleness_discount_factor: 0.2
  aggregator: 'goal_achieved'
  broadcast_manner: 'after_receiving'
  min_received_num: 10
  staleness_toleration: 10