Efficient Scheduling for Multi-Job Vertical Federated Learning
Jingyao Yang, Juncheng Jia, Tao Deng, Mianxiong Dong · 2024
In recent years, federated learning has emerged as an effective approach for the collaborative learning of decentralized data. Vertical federated learning (VFL) is a scenario of federated learning for cross-silo cooperation, where multiple parties with different features about the same set of data jointly train machine learning models without exposing their raw data. Most existing works of VFL focus on a single-job training of one machine learning model. In this paper, we propose a new framework for multi-job VFL, where multiple independent models are trained simultaneously in a cross-silo environment. We formulate the multi-job VFL scheduling problem and propose an efficient solution based on the rolling horizon method. We conduct extensive experiment to evaluate the performance of the solution. The experimental results show that our algorithm outperforms the other baseline algorithms.