Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features

This is an official implementation of the following paper:

Xinyi Shang, Yang Lu*, Gang Huang, and Hanzi Wang.

Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features

International Joint Conference on Artificial Intelligence (IJCAI), 2022

Abstract: Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is the co-occurrence of data heterogeneity and long-tail distribution, which frequently appears in real FL applications. In this paper, we first reveal an intriguing fact that the biased classifier is the primary factor leading to the poor performance of the global model. Motivated by the above finding, we propose a novel and privacy-preserving FL method for heterogeneous and long-tailed data via Classifier Re-training with Federated Features (CReFF). The classifier re-trained on federated features can produce comparable performance as the one re-trained on real data in a privacy-preserving manner without information leakage of local data or class distribution. Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data. Comparative results with the state-of-the-art FL methods also validate the superiority of CReFF.

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.
match_epoch	Number of optimizing federated features.
crt_epoch	Number of re-training classifier.
ipc	Number of federated features per class.
lr_local_training	Learning rate of client updating.
lr_feature	Learning rate of federated features optimization.
lr_net	Learning rate of classifier re-training
non_iid_alpha	Control the degree of heterogeneity.
imb_factor	Control the degree of imbalance.

Usage

Here is an example to run CReFF on CIFAR-10 with imb_factor=0.01:

python main.py --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 \