A Survey of Federated Evaluation in Federated Learning
Abstract
In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work. This is because clients do not expose their original data to preserve data privacy. Federated evaluation plays a vital role in client selection, incentive mechanism design, malicious attack detection, etc. In this paper, we provide the first comprehensive survey of existing federated evaluation methods. Moreover, we explore various applications of federated evaluation for enhancing FL performance and finally present future research directions by envisioning some challenges.
Cite
Text
Soltani et al. "A Survey of Federated Evaluation in Federated Learning." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/758Markdown
[Soltani et al. "A Survey of Federated Evaluation in Federated Learning." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/soltani2023ijcai-survey/) doi:10.24963/IJCAI.2023/758BibTeX
@inproceedings{soltani2023ijcai-survey,
title = {{A Survey of Federated Evaluation in Federated Learning}},
author = {Soltani, Behnaz and Zhou, Yipeng and Haghighi, Venus and Lui, John C. S.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {6769-6777},
doi = {10.24963/IJCAI.2023/758},
url = {https://mlanthology.org/ijcai/2023/soltani2023ijcai-survey/}
}