A Communication Efficient Vertical Federated Learning Framework.
Year: 2019
Type: preprint
Abstract: One critical challenge for applying today's Artificial Intelligence (AI)technologies to real-world applications is the common existence of data silos
across different organizations. Due to legal, privacy and other practical
constraints, data from different organizations cannot be easily integrated.
Federated learning (FL), especially the vertical FL (VFL), allows multiple
parties having different sets of attributes about the same user collaboratively
build models while preserving user privacy. However, communication overhead is
a principal bottleneck since the existing VFL protocols require per-iteration
communications among all parties. In this paper, we propose the Federated
Stochastic Block Coordinate Descent (FedBCD) to effectively reduce the
communication rounds for VFL. We show that when the batch size, sample size,
and the local iterations are selected appropriately, the algorithm requires
$\mathcal{O}(\sqrt{T})$ communication rounds to achieve
$\mathcal{O}(1/\sqrt{T})$ accuracy. Finally, we demonstrate the performance of
FedBCD on several models and datasets, and on a large-scale industrial platform
for VFL.
Source: arXiv (Cornell University)
Authors: Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, Qiang Yang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2996786840