As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G). To this end, in this second part we introduce and optimize a novel communication protocol for WFL networks, that is based on non-orthogonal multiple access (NOMA). More specifically, the Compute-then-Transmit NOMA (CT-NOMA) protocol is introduced, where users terminate concurrently the local model training and then simultaneously transmit the trained parameters to the central server. Moreover, two different detection schemes for the mitigation of inter-user interference in NOMA are considered and evaluated, which correspond to fixed and variable decoding order during the successive interference cancellation process. Furthermore, the computation and communication resources are jointly optimized for both considered schemes, with the aim to minimize the total delay during a WFL communication round. Finally, the simulation results verify the effectiveness of CT-NOMA in terms of delay reduction, compared to the considered benchmark that is based on time-division multiple access.
翻译:正如在这项工作的第一部分中所讨论的那样,利用先进的多种存取协议以及联合优化通信和计算资源有助于减少无线联合学习(WFL)的延误,这对将WFL有效纳入第六代无线网络(6G)至关重要。为此,在第二部分中,我们采用并优化WFL网络的新颖通信协议,以非横向多重存取(NOMA)为基础。更具体地说,引入了计算-自动传输NOMA(CT-NOMA)协议,用户同时终止当地模式培训,然后将经过培训的参数传送到中央服务器。此外,审议和评价了两种不同的检测计划,以缓解NOMA用户之间的干扰,这两类计划与连续取消干扰过程中固定和可变的解码顺序相对应。此外,计算和通信资源在两种考虑的计划中都得到优化,目的是尽量减少WFLL通信回合中的全部拖延。最后,模拟结果核实CT-NOMA在延迟减少方面的效力,与考虑的多重存取基准相比较。