# Federated learning with multichannel ALOHA

arXiv: https://arxiv.org/abs/1912.06273 · HTML: https://mlsanity.com/paper/1912.06273/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

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

- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA](https://mlsanity.com/paper/2309.06033/) (2023)
- [Federated Learning-Distillation Alternation for Resource-Constrained IoT](https://mlsanity.com/paper/2505.20456/) (2025)
- [Age-of-Gradient Updates for Federated Learning over Random Access Channels](https://mlsanity.com/paper/2410.11986/) (2024)

## Abstract (arXiv)

In this paper, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets. We show that multichannel random access can provide a better performance than sequential polling when some users are unable to compute local updates (due to other tasks) or in dormant state. In addition, for better aggregation in federated learning, the access probabilities of users can be optimized for given local updates. To this end, we formulate an optimization problem and show that a distributed approach can be used within federated learning to adaptively decide the access probabilities.