2021/09/14 by Chanho Park, Seunghoon Lee, Park, Chanho +3 · 1 citation
Computer Science · Engineering · #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #Signal Processing (eess.SP) #Wireless Communication Security Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.06579
openalex publication_date 2021/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we consider the problem of wireless federated learning based on sign stochastic gradient descent (signSGD) algorithm via a multiple access channel. When sending locally computed gradient's sign information, each mobile device requires to apply precoding to circumvent wireless fading effects. In practice, however, acquiring perfect knowledge of channel state information (CSI) at all mobile devices is infeasible. In this paper, we present a simple yet effective precoding method with limited channel knowledge, called sign-alignment precoding. The idea of sign-alignment precoding is to protect sign-flipping errors from wireless fadings. Under the Gaussian prior assumption on the local gradients, we also derive the mean squared error (MSE)-optimal aggregation function called Bayesian over-the-air computation (BayAirComp). Our key finding is that one-bit precoding with BayAirComp aggregation can provide a better learning performance than the existing precoding method even using perfect CSI with AirComp aggregation.