Recent years have witnessed a large amount of decentralized data in various
(edge) devices of end-users, while the decentralized data aggregation remains
complicated for machine learning jobs because of regulations and laws. As a
practical approach to handling decentralized data, Federated Learning (FL)
enables collaborative global machine learning model training without sharing
sensitive raw data. The servers schedule devices to jobs within the training
process of FL. In contrast, device scheduling with multiple jobs in FL remains
a critical and open problem. In this paper, we propose a novel multi-job FL
framework, which enables the training process of multiple jobs in parallel. The
multi-job FL framework is composed of a system model and a scheduling method.
The system model enables a parallel training process of multiple jobs, with a
cost model based on the data fairness and the training time of diverse devices
during the parallel training process. We propose a novel intelligent scheduling
approach based on multiple scheduling methods, including an original
reinforcement learning-based scheduling method and an original Bayesian
optimization-based scheduling method, which corresponds to a small cost while
scheduling devices to multiple jobs. We conduct extensive experimentation with
diverse jobs and datasets. The experimental results reveal that our proposed
approaches significantly outperform baseline approaches in terms of training
time (up to 12.73 times faster) and accuracy (up to 46.4% higher).

本研究提出了一个新颖的多工作联邦学习框架，通过智能调度方法实现了分散数据的并行训练过程，实验证明相比基线方法，其训练时间更快（最高可达 12.73 倍）且准确率更高（最高 46.4%）。