Byzantine Resilient and Fast Federated Few-Shot Learning
Abstract
This work introduces a Byzantine resilient solution for learning low-dimensional linear representation. Our main contribution is the development of a provably Byzantine-resilient AltGDmin algorithm for solving this problem in a federated setting. We argue that our solution is sample-efficient, fast, and communicationefficient. In solving this problem, we also introduce a novel secure solution to the federated subspace learning meta-problem that occurs in many different applications.
Cite
Text
Singh and Vaswani. "Byzantine Resilient and Fast Federated Few-Shot Learning." International Conference on Machine Learning, 2024.Markdown
[Singh and Vaswani. "Byzantine Resilient and Fast Federated Few-Shot Learning." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/singh2024icml-byzantine/)BibTeX
@inproceedings{singh2024icml-byzantine,
title = {{Byzantine Resilient and Fast Federated Few-Shot Learning}},
author = {Singh, Ankit Pratap and Vaswani, Namrata},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {45696-45706},
volume = {235},
url = {https://mlanthology.org/icml/2024/singh2024icml-byzantine/}
}