FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation

TL;DR

FEDIC introduces a federated learning approach that effectively handles the combined challenges of non-IID and long-tailed data distributions by using model ensemble and calibrated distillation techniques.

Contribution

The paper proposes FEDIC, a novel federated learning method that jointly addresses non-IID and long-tailed data issues through ensemble and calibrated distillation.

Findings

FEDIC outperforms state-of-the-art federated learning methods on long-tailed datasets.

The approach effectively mitigates bias caused by long-tailed class distributions.

FEDIC maintains high accuracy in highly non-IID settings.

Abstract

Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data. Nevertheless, dealing with non-IID data is one of the most challenging problems for federated learning. Researchers have proposed a variety of methods to eliminate the negative influence of non-IIDness. However, they only focus on the non-IID data provided that the universal class distribution is balanced. In many real-world applications, the universal class distribution is long-tailed, which causes the model seriously biased. Therefore, this paper studies the joint problem of non-IID and long-tailed data in federated learning and proposes a corresponding solution called Federated Ensemble Distillation with Imbalance Calibration (FEDIC). To deal with non-IID data, FEDIC uses model ensemble to take advantage of the diversity of models trained on non-IID data. Then, a new distillation method with logit adjustment and calibration gating network is proposed to solve the long-tail problem effectively. We evaluate FEDIC on CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT with a highly non-IID experimental setting, in comparison with the state-of-the-art methods of federated learning and long-tail learning. Our code is available at https://github.com/shangxinyi/FEDIC.