The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over siloed data centers is motivating renewed interest in the collaborative training of a shared model by multiple individual clients via federated learning (FL). To improve the communication efficiency of FL implementations in wireless systems, recent works have proposed compression and dimension reduction mechanisms, along with digital and analog transmission schemes that account for channel noise, fading, and interference. The prior art has mainly focused on star topologies consisting of distributed clients and a central server. In contrast, this paper studies FL over wireless device-to-device (D2D) networks by providing theoretical insights into the performance of digital and analog implementations of decentralized stochastic gradient descent (DSGD). First, we introduce generic digital and analog wireless implementations of communication-efficient DSGD algorithms, leveraging random linear coding (RLC) for compression and over-the-air computation (AirComp) for simultaneous analog transmissions. Next, under the assumptions of convexity and connectivity, we provide convergence bounds for both implementations. The results demonstrate the dependence of the optimality gap on the connectivity and on the signal-to-noise ratio (SNR) levels in the network. The analysis is corroborated by experiments on an image-classification task.

翻译：互联网连接装置和云计算应用程序在分散的数据中心上的扩散,再次激发了对多个客户通过联合学习(FL)对一个共同模式的合作培训的兴趣。为提高无线系统FL实施功能的通信效率,最近的工作提出了压缩和尺寸减少机制,以及数字和模拟传输计划,其中考虑到频道噪音、消退和干扰。先前的艺术主要侧重于由分布式客户和一个中央服务器组成的恒星表面学。与此形成对照的是,本文研究无线设备对装置网络的FL(D2D)网络的FL(FL)对无线设备对装置对装置网络的连接,方法是提供对分散式梯度梯度下降(DSGD)的数字和模拟实施绩效的理论洞察。首先,我们采用了通用的数字和模拟无线实施通信效率DSGD算法的压缩和尺寸减少机制,利用随机线性编码(RLC)进行压缩和超空测算(Airconcomm),随后,根据调和连接性假设,我们为这两个网络的实施提供了趋同的连接界限。在最优化的网络和图像分析中显示最佳连接程度的信号比重。