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In this paper, we develop a dynamic detection network (DDNet) based detectorfor multiple-input multiple-output (MIMO) systems. By constructing an improvedDetNet (IDetNet) detector and the OAMPNet detector as two independent networkbranches, the DDNet detector performs sample-wise dynamic routing to adaptivelyselect a better one between the IDetNet and the OAMPNet detectors for everysamples under different system conditions. To avoid the prohibitivetransmission overhead of dataset collection in centralized learning (CL), wepropose the federated averaging (FedAve)-DDNet detector, where all raw data arekept at local clients and only locally trained model parameters are transmittedto the central server for aggregation. To further reduce the transmissionoverhead, we develop the federated gradient sparsification (FedGS)-DDNetdetector by randomly sampling gradients with elaborately calculated probabilitywhen uploading gradients to the central server. Based on simulation results,the proposed DDNet detector consistently outperforms other detectors under allsystem conditions thanks to the sample-wise dynamic routing. Moreover, thefederated DDNet detectors, especially the FedGS-DDNet detector, can reduce thetransmission overhead by at least 25.7\% while maintaining satisfactorydetection accuracy.

Federated Dynamic Neural Network for Deep MIMO Detection