# Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification

arXiv: https://arxiv.org/abs/2203.12957 · HTML: https://mlsanity.com/paper/2203.12957/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Communication Trade-offs in Federated Learning of Spiking Neural Networks](https://mlsanity.com/paper/2303.00928/) (2023)
- [Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?](https://mlsanity.com/paper/2310.03410/) (2023)
- [Efficient Wireless Federated Learning via Low-Rank Gradient Factorization](https://mlsanity.com/paper/2401.07496/) (2024)

## Abstract (arXiv)

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.