# Towards Energy-Aware Federated Learning on Battery-Powered Clients

arXiv: https://arxiv.org/abs/2208.04505 · HTML: https://mlsanity.com/paper/2208.04505/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Federated Adversarial Domain Adaptation](https://mlsanity.com/paper/1911.02054/) (2019)
- [Distributed Mean Estimation with Limited Communication](https://mlsanity.com/paper/1611.00429/) (2016)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Training Production Language Models without Memorizing User Data](https://mlsanity.com/paper/2009.10031/) (2020)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity](https://mlsanity.com/paper/2012.14453/) (2020)
- [Can Federated Learning Save The Planet?](https://mlsanity.com/paper/2010.06537/) (2020)
- [Rethinking gradient sparsification as total error minimization](https://mlsanity.com/paper/2108.00951/) (2021)
- [AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning](https://mlsanity.com/paper/2107.08147/) (2021)
- [On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning](https://mlsanity.com/paper/1911.08250/) (2019)
- [An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems](https://mlsanity.com/paper/2101.10761/) (2021)
- [Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity](https://mlsanity.com/paper/2005.12326/) (2020)
- [On the Impact of Device and Behavioral Heterogeneity in Federated Learning](https://mlsanity.com/paper/2102.07500/) (2021)

## Built on by (most cited first, 11 of 11)

- [FedZero: Leveraging Renewable Excess Energy in Federated Learning](https://mlsanity.com/paper/2305.15092/) (2023)
- [CC-FedAvg: Computationally Customized Federated Averaging](https://mlsanity.com/paper/2212.13679/) (2022)
- [Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data](https://mlsanity.com/paper/2306.10848/) (2023)
- [An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge](https://mlsanity.com/paper/2309.00022/) (2023)
- [Energy-Aware Decentralized Learning with Intermittent Model Training](https://mlsanity.com/paper/2407.01283/) (2024)
- [SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment](https://mlsanity.com/paper/2407.18387/) (2024)
- [Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning](https://mlsanity.com/paper/2509.13933/) (2025)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [Learn More by Using Less: Distributed Learning with Energy-Constrained Devices](https://mlsanity.com/paper/2412.02289/) (2024)
- [Semi-decentralized Federated Time Series Prediction with Client Availability Budgets](https://mlsanity.com/paper/2509.03660/) (2025)
- [Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation](https://mlsanity.com/paper/2510.00976/) (2025)

## Abstract (arXiv)

Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. However, despite the remarkable advancement, this paradigm comes with various challenges. Specifically, in large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. Moreover, energy consumption across these battery-constrained devices is largely unexplored and a limitation for wide-adoption of FL. To address this issue, we develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices. EAFL is a power-aware training algorithm that cherry-picks clients with higher battery levels in conjunction with its ability to maximize the system efficiency. Our design jointly minimizes the time-to-accuracy and maximizes the remaining on-device battery levels. EAFLimproves the testing model accuracy by up to 85% and decreases the drop-out of clients by up to 2.45×.