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arXiv 2102.10591cs.NI
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CFLMEC：面向移动边缘计算的协同联邦学习

CFLMEC: Cooperative Federated Learning for Mobile Edge Computing

Xinghan Wang, Xiaoxiong Zhong, Yuanyuan Yang, Tingting Yang

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10 pages, 8 figures, submitted to a conference
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摘要：

我们研究了移动边缘计算设备之间的协作联邦学习框架，名为CFLMEC，其中设备共存于共享频谱中。针对协作式联邦学习框架的时均网络吞吐量和频谱稀缺性问题，重点研究了最大化边缘服务器或邻近设备的接入数据，填补了联邦学习设备通信资源分配的空白。在CFLMEC中，设备可以通过中继竞争的方式将本地模型传输到对应的设备或边缘服务器，并且我们使用分解方法来解决考虑子信道上的最大数据率的资源优化问题，信道重用和无线资源分配的方法，建立了一个原始-对偶学习框架和批量梯度下降，利用过时的信息学习动态网络，并预测子网络，信道条件为了最大化设备的吞吐量，我们提出了通信资源分配算法和没有足够的子信道的强烈依赖于边缘服务器（SR）的蜂窝链路，和干扰感知通信资源分配算法的边缘服务器（LR）的依赖性较低的D2 D链路。大量的仿真结果表明，CFLMEC可以实现最高的吞吐量的本地设备相比，现有的工作，同时限制的子信道的数量。

英文摘要：

We investigate a cooperative federated learning framework among devices for mobile edge computing, named CFLMEC, where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC, devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve the resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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