Semantic Scholar Open Access 2021 1211 sitasi

Federated Learning on Non-IID Data: A Survey

Hangyu Zhu Jinjin Xu Shiqing Liu Yaochu Jin

Lihat Sumber DOI

Abstrak

Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than those trained in the standard centralized learning mode, especially when the training data are not independent and identically distributed (Non-IID) on the local devices. In this survey, we pro-vide a detailed analysis of the influence of Non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning. In addition, cur-rent research work on handling challenges of Non-IID data in federated learning are reviewed, and both advantages and disadvantages of these approaches are discussed. Finally, we suggest several future research directions before concluding the paper.

Topik & Kata Kunci

Computer Science

Penulis (4)

Hangyu Zhu

Jinjin Xu

Shiqing Liu

Yaochu Jin

Format Sitasi

APA MLA BibTeX

Zhu, H., Xu, J., Liu, S., Jin, Y. (2021). Federated Learning on Non-IID Data: A Survey. https://doi.org/10.1016/j.neucom.2021.07.098

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.neucom.2021.07.098

Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Total Sitasi: 1211×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.neucom.2021.07.098
Akses: Open Access ✓