FedLF

Official codes for ACML '24 research paper: FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning.

Dependencies

• python 3.7.9 (Anaconda)
• PyTorch 1.7.0
• torchvision 0.8.1
• CUDA 11.2
• cuDNN 8.0.4

Dataset

• CIFAR-10
• CIFAR-100
• ImageNet-LT

Parameters

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.

Usage

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 \
--lr_local_training=0.1 \
--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 \
--lr_local_training=0.1 \
--non-iid_alpha=0.5 \
--imb_factor=0.01 | tee creff_imb001_cifar10lt.log

Note:

In addition, we will launch a Federated Long-Tailed Learning algorithm library. Please stay tuned. https://github.com/18sym/Federated-Long-Tailed-Learning