MIMO Over-The-Air Federated Learning With Spiking Neural Network Via Lattice Code

Spiking neural networks (SNNs) have emerged as an energy-efficient alternative to the traditional artificial neural networks (ANNs) which are compute-intensive. This paper proposes a novel MIMO over-the-air federated learning scheme trained on SNNs using lattice code. Based on the lattice structure, we design a reliable transceiver with lattice quantizer that can combat the noise and interference from the devices. We further derive a convergence analysis of the proposed method considering the nondifferentiable spikes of SNNs. The experimental results verify that the proposed method is effective by showing that the proposed method can achieve comparable accuracy to the ideal benchmarks and outperform the existing approach by employing a small number of antennas at the server and devices. We also show that SNNs are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$23.08 \times$</tex> more energy-efficient than ANNs.