Digital versus Analog Transmissions for Federated Learning over Wireless
  Networks
Jiacheng Yao Wei Xu Zhaohui Yang Xiaohu You Mehdi Bennis H. Vincent Poor
In this paper, we quantitatively compare these two effective communication
schemes, i.e., digital and analog ones, for wireless federated learning (FL)
over resource-constrained networks, highlighting their essential differences as
well as their respective application scenarios. We first examine both digital
and analog transmission methods, together with a unified and fair comparison
scheme under practical constraints. A universal convergence analysis under
various imperfections is established for FL performance evaluation in wireless
networks. These analytical results reveal that the fundamental difference
between the two paradigms lies in whether communication and computation are
jointly designed or not. The digital schemes decouple the communication design
from specific FL tasks, making it difficult to support simultaneous uplink
transmission of massive devices with limited bandwidth. In contrast, the analog
communication allows over-the-air computation (AirComp), thus achieving
efficient spectrum utilization. However, computation-oriented analog
transmission reduces power efficiency, and its performance is sensitive to
computational errors. Finally, numerical simulations are conducted to verify
these theoretical observations.