# Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning

arXiv: https://arxiv.org/abs/2504.12181 · HTML: https://mlsanity.com/paper/2504.12181/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](https://mlsanity.com/paper/2004.04314/) (2020)
- [On the Convergence of Federated Averaging with Cyclic Client Participation](https://mlsanity.com/paper/2302.03109/) (2023)
- [Federated Learning over Energy Harvesting Wireless Networks](https://mlsanity.com/paper/2106.08809/) (2021)
- [Green Federated Learning: A new era of Green Aware AI](https://mlsanity.com/paper/2409.12626/) (2024)
- [Over-the-Air Federated Learning with Energy Harvesting Devices](https://mlsanity.com/paper/2205.12869/) (2022)
- [Green Federated Learning](https://mlsanity.com/paper/2303.14604/) (2023)
- [Energy-Harvesting Distributed Machine Learning](https://mlsanity.com/paper/2102.05639/) (2021)
- [Fast-Convergent Federated Learning via Cyclic Aggregation](https://mlsanity.com/paper/2210.16520/) (2022)
- [Version age-based client scheduling policy for federated learning](https://mlsanity.com/paper/2402.05407/) (2024)
- [DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks](https://mlsanity.com/paper/2406.13533/) (2024)
- [On Federated Learning with Energy Harvesting Clients](https://mlsanity.com/paper/2202.06105/) (2022)
- [Federated Learning With Energy Harvesting Devices: An MDP Framework](https://mlsanity.com/paper/2405.10513/) (2024)
- [From AoI to QVAoI: Query-Based Semantics-Aware Scheduling for Energy-Harvesting IoT Systems](https://mlsanity.com/paper/2407.08587/) (2024)

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

- [Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling](https://mlsanity.com/paper/2511.11949/) (2025)
- [Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning](https://mlsanity.com/paper/2512.01983/) (2025)

## Abstract (arXiv)

Federated Learning (FL) has emerged as a promising framework for distributed learning, but its growing complexity has led to significant energy consumption, particularly from computations on the client side. This challenge is especially critical in energy-harvesting FL (EHFL) systems, where device availability fluctuates due to limited and time-varying energy resources. We propose FedBacys, a battery-aware FL framework that introduces cyclic client participation based on users' battery levels to cope with these issues. FedBacys enables clients to save energy and strategically perform local training just before their designated transmission time by clustering clients and scheduling their involvement sequentially. This design minimizes redundant computation, reduces system-wide energy usage, and improves learning stability. Our experiments demonstrate that FedBacys outperforms existing approaches in terms of energy efficiency and performance consistency, exhibiting robustness even under non-i.i.d. training data distributions and with very infrequent battery charging. This work presents the first comprehensive evaluation of cyclic client participation in EHFL, incorporating both communication and computation costs into a unified, resource-aware scheduling strategy.