  We provide the optimal receive combining strategy for federated learning in
multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows
the clients to perform individual gradient sparsification which greatly
improves performance in scenarios with heterogeneous (non i.i.d.) training
data. The proposed method beats the benchmark by a wide margin.
