Joint Participant Selection and Learning Scheduling for Multi-Model Federated Edge Learning
Joint Participant Selection and Learning Scheduling for Multi-Model Federated Edge Learning
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DOI：
10.1109/mass56207.2022.00081
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Xinliang Wei;Jiyao Liu;Yu Wang
中科院分区：
文献类型：
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作者：
Xinliang Wei;Jiyao Liu;Yu Wang
由于边缘计算是对云的补充，可以在网络边缘实现计算服务，因此联邦学习（FL）也可以从附近的边缘计算基础设施中受益。然而，大多数联邦边缘学习（FEL）的现有工作主要集中在一个共享的全局模型在边缘系统的联邦训练。在真实的边缘计算场景中，可能存在由不同实体拥有并由不同应用使用的多个不同FL模型。同时训练这些模型会竞争共享边缘系统中的计算和网络资源。因此，在这项工作中，我们考虑了多模型联合边缘学习，其中多个FEL模型在边缘网络中进行训练，边缘服务器可以充当这些FEL模型的参数服务器或工作者。我们制定了一个联合参与者选择和学习调度问题，这是一个非线性混合整数规划，旨在最小化所有FEL模型的总成本，同时满足所需的收敛速度的训练FEL模型和约束的边缘资源。然后，我们设计了几个算法，通过解耦原问题成两个或三个子问题，可以分别解决和迭代。大量的模拟与现实世界的训练数据集和FEL模型表明，我们提出的算法可以有效地降低平均总成本的所有FEL模型在多模型FEL设置相比，现有的算法。
As edge computing complements the cloud to enable computational services right at the network edge, federated learning (FL) can also benefit from close-by edge computing infrastructure. However, most prior works on federated edge learning (FEL) mainly focus on one shared global model during the federated training in edge systems. In a real edge computing scenario, there may co-exist multiple various FL models that are owned by different entities and used by different applications. Simultaneously training these models competes both computing and networking resources in the shared edge system. Therefore, in this work, we consider a multi-model federated edge learning where multiple FEL models are being trained in the edge network and edge servers can act as either parameter servers or workers of these FEL models. We formulate a joint participant selection and learning scheduling problem, which is a non-linear mixed-integer program, aiming to minimize the total cost of all FEL models while satisfying the desired convergence rate of trained FEL models and the constrained edge resources. We then design several algorithms by decoupling the original problem into two or three sub-problems which can be solved respectively and iteratively. Extensive simulations with real-world training datasets and FEL models show that our proposed algorithms can efficiently reduce the average total cost of all FEL models in a multi-model FEL setting compared with existing algorithms.