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arXiv 2106.08809cs.ITcs.NImath.IT
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能量收集无线网络中的联邦学习

Federated Learning over Energy Harvesting Wireless Networks

Rami Hamdi, Mingzhe Chen, Ahmed Ben Said, Marwa Qaraqe, H. Vincent Poor

摘要:

在本文中,部署的联邦学习(FL)的能量收集无线网络中,基站(BS)采用大规模多输入多输出(MIMO)服务于一组独立的能量收集源供电的用户。由于一定数量的用户可能无法参与FL由于干扰和能量的限制,联合能量管理和用户调度问题的FL在无线系统中制定。该问题被表述为一个优化问题,其目标是通过优化用户调度来最小化FL训练损失。为了研究发射功率和调度用户数等因素对训练损耗的影响,首先分析了FL算法的收敛速度。给出该分析结果,用户调度和能量管理优化问题可以被分解、简化和求解。此外,通过考虑多个BS来扩展系统模型。因此,联合用户关联和调度问题的FL在无线系统中进行了研究。最佳用户关联问题的解决使用分支定界技术。仿真结果表明,与标准FL算法相比,所提出的用户调度和用户关联算法可以减少训练损失。

英文摘要:

In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) employs massive multiple-input multiple-output (MIMO) to serve a set of users powered by independent energy harvesting sources. Since a certain number of users may not be able to participate in FL due to the interference and energy constraints, a joint energy management and user scheduling problem in FL over wireless systems is formulated. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To find how the factors such as transmit power and number of scheduled users affect the training loss, the convergence rate of the FL algorithm is first analyzed. Given this analytical result, the user scheduling and energy management optimization problem can be decomposed, simplified, and solved. Further, the system model is extended by considering multiple BSs. Hence, a joint user association and scheduling problem in FL over wireless systems is studied. The optimal user association problem is solved using the branch-and-bound technique. Simulation results show that the proposed user scheduling and user association algorithm can reduce training loss compared to a standard FL algorithm.

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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