  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.
