Dynamic Scheduling for Federated Edge Learning with Streaming Data
This work addresses resource allocation challenges in federated edge learning for applications with streaming data, but it is incremental as it builds on existing optimization frameworks.
The paper tackles the problem of efficiently scheduling edge devices in federated learning with streaming data under energy and latency constraints, proposing a dynamic scheduling policy that maximizes time-average data importance and outperforms methods ignoring temporal data correlation.
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.