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Recent years have witnessed a large amount of decentralized data in various(edge) devices of end-users, while the decentralized data aggregation remainscomplicated for machine learning jobs because of regulations and laws. As apractical approach to handling decentralized data, Federated Learning (FL)enables collaborative global machine learning model training without sharingsensitive raw data. The servers schedule devices to jobs within the trainingprocess of FL. In contrast, device scheduling with multiple jobs in FL remainsa critical and open problem. In this paper, we propose a novel multi-job FLframework, which enables the training process of multiple jobs in parallel. Themulti-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 acost model based on the data fairness and the training time of diverse devicesduring the parallel training process. We propose a novel intelligent schedulingapproach based on multiple scheduling methods, including an originalreinforcement learning-based scheduling method and an original Bayesianoptimization-based scheduling method, which corresponds to a small cost whilescheduling devices to multiple jobs. We conduct extensive experimentation withdiverse jobs and datasets. The experimental results reveal that our proposedapproaches significantly outperform baseline approaches in terms of trainingtime (up to 12.73 times faster) and accuracy (up to 46.4% higher).

Multi-Job Intelligent Scheduling with Cross-Device Federated Learning