Official codes for ACML '24 research paper: FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning.
The following arguments to the ./options.py file control the important parameters of the experiment.
| Argument | Description |
|---|---|
num_classes |
Number of classes |
num_clients |
Number of all clients. |
num_online_clients |
Number of participating local clients. |
num_rounds |
Number of communication rounds. |
num_epochs_local_training |
Number of local epochs. |
batch_size_local_training |
Batch size of local training. |
lr_local_training |
Learning rate of client updating. |
non_iid_alpha |
Control the degree of heterogeneity. |
imb_factor |
Control the degree of imbalance. |
Here is an example to run FedLF on CIFAR-10 with imb_factor=0.01:
python main.py --algorithm fedlf \
--num_classrs=10 \
--num_clients=20 \
--num_online_clients=8 \
--num_rounds=200 \
--num_epochs_local_training=10 \
--batch_size_local_training=32 \
--match_epoch=100 \
--ctr_epoch=300 \
--ipc=100 \
--lr_local_training=0.1 \
--lr_feature=0.1 \
--lr_net=0.01 \
--non-iid_alpha=0.5 \
--imb_factor=0.01 \ In Linux environments, here is an example to run CFedLF on CIFAR-10 with imb_factor=0.01 and save the output log to file:
python main.py --algorithm fedlf \
--num_classrs=10 \
--num_clients=20 \
--num_online_clients=8 \
--num_rounds=200 \
--num_epochs_local_training=10 \
--batch_size_local_training=32 \
--match_epoch=100 \
--ctr_epoch=300 \
--ipc=100 \
--lr_local_training=0.1 \
--lr_feature=0.1 \
--lr_net=0.01 \
--non-iid_alpha=0.5 \
--imb_factor=0.01 | tee creff_imb001_cifar10lt.log