# Over-the-Air Federated Learning with Energy Harvesting Devices

arXiv: https://arxiv.org/abs/2205.12869 · HTML: https://mlsanity.com/paper/2205.12869/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2022.

## Built on by (most cited first, 8 of 8)

- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Federated Learning With Energy Harvesting Devices: An MDP Framework](https://mlsanity.com/paper/2405.10513/) (2024)
- [Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA](https://mlsanity.com/paper/2309.06033/) (2023)
- [Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices](https://mlsanity.com/paper/2501.18298/) (2025)
- [Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning](https://mlsanity.com/paper/2504.12181/) (2025)
- [Differentially Private Wireless Federated Learning Using Orthogonal Sequences](https://mlsanity.com/paper/2306.08280/) (2023)
- [Random Orthogonalization for Federated Learning in Massive MIMO Systems](https://mlsanity.com/paper/2210.09881/) (2022)
- [Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization](https://mlsanity.com/paper/2608.01426/) (2026)

## Abstract (arXiv)

We consider federated edge learning (FEEL) among mobile devices that harvest the required energy from their surroundings, and share their updates with the parameter server (PS) through a shared wireless channel. In particular, we consider energy harvesting FL with over-the-air (OTA) aggregation, where the participating devices perform local computations and wireless transmission only when they have the required energy available, and transmit the local updates simultaneously over the same channel bandwidth. In order to prevent bias among heterogeneous devices, we utilize a weighted averaging with respect to their latest energy arrivals and data cardinalities. We provide a convergence analysis and carry out numerical experiments with different energy arrival profiles, which show that even though the proposed scheme is robust against devices with heterogeneous energy arrivals in error-free scenarios, we observe a 5-10% performance loss in energy harvesting OTA FL.