An Autoencoder-Based Constellation Design for AirComp in Wireless
  Federated Learning
Yujia Mu Xizixiang Wei Cong Shen
Wireless federated learning (FL) relies on efficient uplink communications to
aggregate model updates across distributed edge devices. Over-the-air
computation (a.k.a. AirComp) has emerged as a promising approach for addressing
the scalability challenge of FL over wireless links with limited communication
resources. Unlike conventional methods, AirComp allows multiple edge devices to
transmit uplink signals simultaneously, enabling the parameter server to
directly decode the average global model. However, existing AirComp solutions
are intrinsically analog, while modern wireless systems predominantly adopt
digital modulations. Consequently, careful constellation designs are necessary
to accurately decode the sum model updates without ambiguity. In this paper, we
propose an end-to-end communication system supporting AirComp with digital
modulation, aiming to overcome the challenges associated with accurate decoding
of the sum signal with constellation designs. We leverage autoencoder network
structures and explore the joint optimization of transmitter and receiver
components. Our approach fills an important gap in the context of accurately
decoding the sum signal in digital modulation-based AirComp, which can advance
the deployment of FL in contemporary wireless systems.