Federated Learning for Intelligent Resources Allocation in Internet of Things

Mahmoud Ismail, S. Zaki
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引用次数: 0

Abstract

By using federated learning (FL), multiple Internet-of-Things (IoT) devices can construct a shared learning model without sending raw data to a centralized server. While FL has come a long way, it still has a ways to go. Issues such as heterogeneous user equipment (UEs) and data that is not independently and uniformly distributed are still obstacles. Facilitating a numerous UEs to participate in the learning in each cycle poses a possible problem of the huge communication budget. A weighted adjoining factor is presented to the localized gradient descent, generalizing the present FedAvg to solve these concerns. At the start of each global round, the proposed FL method randomly selects a fraction of the UEs to perform stochastic gradient descent in parallel. Then, we utilize the suggested FL method in cellular IoT to reduce either total power usage or execution duration of FL, in which a straightforward but effective path-following method is constructed for its explanations. At last, obtained simulations on poorly balanced data are presented to show that the presented FL algorithm is superior to FedAvg in terms of performance with respect to fast convergence. Moreover, they show that the suggested algorithm needs significantly less time and energy to train than the FL algorithm does when users contribute heavily to the learning process. These findings provide strong support for the suggested FL algorithm as a potential paradigm change for training mobile IoT networks with limited bandwidth.
面向物联网智能资源配置的联邦学习
通过使用联邦学习(FL),多个物联网(IoT)设备可以构建共享学习模型,而无需将原始数据发送到集中式服务器。虽然FL已经走了很长一段路,但它还有很长的路要走。异构用户设备(ue)和数据不是独立和均匀分布等问题仍然是障碍。在每个周期中,要让众多的ue参与到学习中来,可能会带来巨大的传播预算问题。针对局部梯度下降问题,提出了加权邻接因子,对现有的梯度下降算法进行了推广,解决了这些问题。在每轮全局回合开始时,该方法随机选择一部分ue并行执行随机梯度下降。然后,我们在蜂窝物联网中使用建议的FL方法来减少FL的总功耗或执行时间,其中构建了一个简单而有效的路径跟踪方法来解释其解释。最后,在差平衡数据上进行了仿真,结果表明该算法在快速收敛方面优于fedag算法。此外,他们表明,当用户对学习过程贡献很大时,所建议的算法所需的训练时间和精力要比FL算法少得多。这些发现为建议的FL算法作为训练带宽有限的移动物联网网络的潜在范式变化提供了强有力的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.70
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