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• W4385489093 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." @default.
• W4385489093 created "2023-08-03" @default.
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• W4385489093 date "2023-06-04" @default.
• W4385489093 modified "2023-09-26" @default.
• W4385489093 title "Dynamic Scheduling For Federated Edge Learning With Streaming Data" @default.
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• W4385489093 doi "https://doi.org/10.1109/icasspw59220.2023.10193322" @default.
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