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

Xinyi Shang, Yang Lu*, Yiu-ming Cheung, Hanzi Wang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

7 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Multimedia and Expo (ICME)
PublisherIEEE
Number of pages6
ISBN (Electronic)9781665485630
ISBN (Print)9781665485647
DOIs
Publication statusPublished - 18 Jul 2022
Event2022 IEEE International Conference on Multimedia and Expo, ICME 2022 - Taipei, Taiwan
Duration: 18 Jul 202222 Jul 2022
https://ieeexplore.ieee.org/xpl/conhome/9859562/proceeding

Publication series

NameIEEE International Conference on Multimedia and Expo (ICME)
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2022 IEEE International Conference on Multimedia and Expo, ICME 2022
CityTaipei, Taiwan
Period18/07/2222/07/22
Internet address

User-Defined Keywords

  • Federated learning
  • Non-IID
  • Long-tailed learning
  • Distillation

Fingerprint

Dive into the research topics of 'FEDIC: Federated Learning on Non-IID and Long-tailed Data via Calibrated Distillation'. Together they form a unique fingerprint.

Cite this