# Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback

arXiv: https://arxiv.org/abs/2506.04113 · HTML: https://mlsanity.com/paper/2506.04113/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [A Byzantine-Robust Federated Learning with Nesterov Momentum](https://mlsanity.com/paper/2511.02657/) (2025)
- [CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks](https://mlsanity.com/paper/2510.20219/) (2025)
- [Fed-PELAD: Communication-Efficient Federated Learning for Massive MIMO CSI Feedback with Personalized Encoders and a LoRA-Adapted Shared Decoder](https://mlsanity.com/paper/2510.25181/) (2025)

## Abstract (arXiv)

Recent advancements have introduced federated machine learning-based channel state information (CSI) compression before the user equipments (UEs) upload the downlink CSI to the base transceiver station (BTS). However, most existing algorithms impose a high communication overhead due to frequent parameter exchanges between UEs and BTS. In this work, we propose a model splitting approach with a shared model at the BTS and multiple local models at the UEs to reduce communication overhead. Moreover, we implant a pipeline module at the BTS to reduce training time. By limiting exchanges of boundary parameters during forward and backward passes, our algorithm can significantly reduce the exchanged parameters over the benchmarks during federated CSI feedback training.