Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity
Xingyu Lu, Hasin Us Sami, Başak Güler
Abstract
Collaborative machine learning enables privacy-preserving training of machine learning models without collecting sensitive client data. Despite recent breakthroughs, communication bottleneck is still a major challenge against its scalability to larger networks. To address this challenge, we propose PICO, the first collaborative learning framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under formal information-theoretic privacy guarantees. Theoretical analysis demonstrates that PICO slashes the communication cost while achieving equal computational complexity, adversary resilience, robustness to client dropouts, and model accuracy to the state-of-the-art. Extensive experiments demonstrate up to 91x reduction in the communication overhead, and up to 7x speed-up in the wall-clock training time compared to the state-of-the-art. As such, PICO addresses a key technical challenge in multi-party collaborative learning, paving the way for future large-scale privacy-preserving learning frameworks.
BibTeX
@InProceedings{pmlr-v206-lu23a,
title = {Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity},
author = {Lu, Xingyu and Sami, Hasin Us and G\"uler, Ba\c{s}ak},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {10566--10593},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/lu23a/lu23a.pdf},
url = {https://proceedings.mlr.press/v206/lu23a.html},
abstract = {Collaborative machine learning enables privacy-preserving training of machine learning models without collecting sensitive client data. Despite recent breakthroughs, communication bottleneck is still a major challenge against its scalability to larger networks. To address this challenge, we propose PICO, the first collaborative learning framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under formal information-theoretic privacy guarantees. Theoretical analysis demonstrates that PICO slashes the communication cost while achieving equal computational complexity, adversary resilience, robustness to client dropouts, and model accuracy to the state-of-the-art. Extensive experiments demonstrate up to 91x reduction in the communication overhead, and up to 7x speed-up in the wall-clock training time compared to the state-of-the-art. As such, PICO addresses a key technical challenge in multi-party collaborative learning, paving the way for future large-scale privacy-preserving learning frameworks.}
}