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arXiv · 2305.01238

Dynamic Scheduling for Federated Edge Learning with Streaming Data

Chung-Hsuan Hu·Zheng Chen·Erik G. Larsson
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Abstract

In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration. We formulate a stochastic network optimization problem for designing a dynamic scheduling policy that maximizes the time-average data importance from scheduled user sets subject to energy consumption and latency constraints. Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data importance, especially when the generation of training data shows strong temporal correlation.

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BibTeXRIS

Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson. 2023-05-02. Dynamic Scheduling for Federated Edge Learning with Streaming Data. https://arxiv.org/abs/2305.01238

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