Multi-Job Intelligent Scheduling with Cross-Device Federated Learning

• URL: http://arxiv.org/abs/2211.13430v1
• Date: Thu, 24 Nov 2022 06:17:40 GMT
• Title: Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
• Authors: Ji Liu, Juncheng Jia, Beichen Ma, Chendi Zhou, Jingbo Zhou, Yang Zhou, Huaiyu Dai, Dejing Dou
• Abstract summary: Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. We propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. 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.
• Score: 65.69079337653994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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