Dynamic Scheduling for Federated Edge Learning with Streaming Data
- URL: http://arxiv.org/abs/2305.01238v1
- Date: Tue, 2 May 2023 07:41:16 GMT
- Title: Dynamic Scheduling for Federated Edge Learning with Streaming Data
- Authors: Chung-Hsuan Hu, Zheng Chen, and Erik G. Larsson
- Abstract summary: 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.
- Score: 56.91063444859008
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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