Reinforcement Learning-Based Network Slice Resource Allocation for Federated Learning Applications
Reinforcement Learning-Based Network Slice Resource Allocation for Federated Learning Applications
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DOI：
10.1109/globecom48099.2022.10001715
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;J. Jue
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文献类型：
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作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;J. Jue
提出了一种网络片的资源分配策略，其中每个网络片支持不同的联合学习任务。当需要训练新的联合学习模型时，建立切片，并在训练完成后释放。其目标是最小化平均网络切片持有时间，同时还提供切片租户之间的公平性，并提高网络效率。我们提出了一种基于强化学习的策略，根据每个联合学习任务的当前状态周期性地重新分配资源。我们提供了两种强化学习模型。第一种模型性能更稳定，并考虑了任务之间的相关性，而第二种模型使用的参数更少，对不同数量的任务具有更强的鲁棒性。这两种方法都比基线启发式方法具有更好的性能。我们还提出了一种缓解各种资源规模影响的方法，使训练变得稳定。
This paper addresses a resource allocation strategy for network slices, where each network slice supports a different federated learning task. A slice is established when a new federated learning model needs to be trained and is released once the training is complete. The goal is to minimize the average network slice holding time while also providing fairness between slice tenants and improving network efficiency. We propose a reinforcement learning-based strategy to periodically reallocate resources according to the current state of each federated learning task. We offer two reinforcement learning models. The first model achieves more stable performance and considers correlations between tasks, while the second model utilizes fewer parameters and is more robust to varying number of tasks. Both approaches have better performance than baseline heuristic methods. We also propose a method to alleviate the effect of various resources scales to make the training stable.