An Autoencoder-Based Constellation Design for Accurate Decoding of Aggregated Model Updates in Wireless Federated Learning

The proposed autoencoder-based communication system enables accurate decoding of the sum of model updates from multiple clients in wireless federated learning, overcoming the challenges associated with existing constellation designs.