# Rate-Convergence Tradeoff of Federated Learning over Wireless Channel

arXiv: https://arxiv.org/abs/2205.04672 · HTML: https://mlsanity.com/paper/2205.04672/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2022.

## Built on by (most cited first, 2 of 2)

- [Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning](https://mlsanity.com/paper/2609.12903/) (2026)
- [Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data](https://mlsanity.com/paper/2609.14246/) (2026)

## Abstract (arXiv)

In this paper, we consider a federated learning problem over wireless channel that takes into account the coding rate and packet transmission errors. Communication channels are modelled as packet erasure channels (PEC), where the erasure probability is determined by the block length, code rate, and signal-to-noise ratio (SNR). To lessen the effect of packet erasure on the FL performance, we propose two schemes in which the central node (CN) reuses either the past local updates or the previous global parameters in case of packet erasure. We investigate the impact of coding rate on the convergence of federated learning (FL) for both short packet and long packet communications considering erroneous transmissions. Our simulation results shows that even one unit of memory has considerable impact on the performance of FL in erroneous communication.