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May 2, 2023 · Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data ...
ABSTRACT. In this work, we consider a Federated Edge Learning (FEEL) sys- tem where training data are randomly generated over time at a set of.
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8 days ago · Our goal is to develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource ...
May 2, 2023 · Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data ...
This work considers an over-the-air FEEL system with analog gradient aggregation, and proposes an energy-aware dynamic device scheduling algorithm to ...
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Nov 12, 2023 · Experimental results show that, under a highly unbalanced local data distribution, the proposed algorithm can increase the accuracy by 4.9% on ...
May 2, 2023 · In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of ...
Abstract—Machine learning and wireless communication tech- nologies are jointly facilitating an intelligent edge, where feder- ated edge learning (FEEL) is ...
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Sep 8, 2023 · It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big ...
7 days ago · Our goal is to develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource ...