  Wireless communications is often subject to channel fading. Various
statistical models have been proposed to capture the inherent randomness in
fading, and conventional model-based receiver designs rely on accurate
knowledge of this underlying distribution, which, in practice, may be complex
and intractable. In this work, we propose a neural network-based symbol
detection technique for downlink fading channels, which is based on the maximum
a-posteriori probability (MAP) detector. To enable training on a diverse
ensemble of fading realizations, we propose a federated training scheme, in
which multiple users collaborate to jointly learn a universal data-driven
detector, hence the name FedRec. The performance of the resulting receiver is
shown to approach the MAP performance in diverse channel conditions without
requiring knowledge of the fading statistics, while inducing a substantially
reduced communication overhead in its training procedure compared to
centralized training.
