论文标题
通过无线渠道联邦学习的速率连接权衡取舍
Rate-Convergence Tradeoff of Federated Learning over Wireless Channel
论文作者
论文摘要
在本文中，我们考虑了无线通道上的联合学习问题，该问题考虑了编码率和数据包传输错误。通信通道被建模为数据包擦除通道（PEC），其中擦除概率由块长度，代码速率和信噪比（SNR）确定。为了降低数据包擦除对FL性能的影响，我们提出了两个方案，其中中央节点（CN）重用过去的本地更新或在数据包擦除时先前的全局参数。我们研究编码率对错误传输的短数据包和长数据包通信的联合学习（FL）的影响。我们的仿真结果表明，即使是一个内存的单位也对错误通信中的FL的性能产生了很大影响。
In this paper, we consider a federated learning problem over wireless channel that takes into account the coding rate and packet transmission errors. Communication channels are modelled as packet erasure channels (PEC), where the erasure probability is determined by the block length, code rate, and signal-to-noise ratio (SNR). To lessen the effect of packet erasure on the FL performance, we propose two schemes in which the central node (CN) reuses either the past local updates or the previous global parameters in case of packet erasure. We investigate the impact of coding rate on the convergence of federated learning (FL) for both short packet and long packet communications considering erroneous transmissions. Our simulation results shows that even one unit of memory has considerable impact on the performance of FL in erroneous communication.
