# Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA

arXiv: https://arxiv.org/abs/2309.06033 · HTML: https://mlsanity.com/paper/2309.06033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data](https://mlsanity.com/paper/2105.07066/) (2021)
- [Federated Learning over Energy Harvesting Wireless Networks](https://mlsanity.com/paper/2106.08809/) (2021)
- [Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer](https://mlsanity.com/paper/2102.12357/) (2021)
- [Over-the-Air Federated Learning with Energy Harvesting Devices](https://mlsanity.com/paper/2205.12869/) (2022)
- [Federated learning with multichannel ALOHA](https://mlsanity.com/paper/1912.06273/) (2019)
- [Massive Wireless Energy Transfer: Enabling Sustainable IoT Towards 6G Era](https://mlsanity.com/paper/1912.05322/) (2019)

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

- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [Federated Learning-Distillation Alternation for Resource-Constrained IoT](https://mlsanity.com/paper/2505.20456/) (2025)

## Abstract (arXiv)

Distributed learning on edge devices has attracted increased attention with the advent of federated learning (FL). Notably, edge devices often have limited battery and heterogeneous energy availability, while multiple rounds are required in FL for convergence, intensifying the need for energy efficiency. Energy depletion may hinder the training process and the efficient utilization of the trained model. To solve these problems, this letter considers the integration of energy harvesting (EH) devices into a FL network with multi-channel ALOHA, while proposing a method to ensure both low energy outage probability and successful execution of future tasks. Numerical results demonstrate the effectiveness of this method, particularly in critical setups where the average energy income fails to cover the iteration cost. The method outperforms a norm based solution in terms of convergence time and battery level.