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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