# A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices

arXiv: https://arxiv.org/abs/2605.27601 · HTML: https://mlsanity.com/paper/2605.27601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [On-Device Machine Learning: An Algorithms and Learning Theory Perspective](https://mlsanity.com/paper/1911.00623/) (2019)
- [Towards Energy Efficient Federated Learning over 5G+ Mobile Devices](https://mlsanity.com/paper/2101.04866/) (2021)
- [AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices](https://mlsanity.com/paper/2301.03062/) (2023)
- [Dissecting the software-based measurement of CPU energy consumption: a comparative analysis](https://mlsanity.com/paper/2401.15985/) (2024)

## Abstract (arXiv)

Estimating CPU power on heterogeneous ARM-based commodity devices is challenging due to limited access to CPU's voltage domains. As a result, state-of-the-art energy-aware Federated Learning (FL) frameworks typically rely on simplified approximate power models to estimate computation energy, rather than the more accurate analytical CMOS-based model. To bridge this gap, we propose a reproducible CPU power estimation methodology combined with a rail-to-cluster mapping technique to retrieve cluster-level supply voltage. We evaluate our approach on two commodity Android devices and show that the analytical model predicts CPU power with errors below 10%, whereas the approximate model incurs errors of up to 959%. Using AnycostFL, a state-of-the-art energy-aware FL framework, we show that the analytical model achieves the same 80% model accuracy while consuming 1.4x less energy than the approximate model. These results highlight that approximate models can severely misestimate computation energy and lead to suboptimal decisions. This work facilitates the use of analytical CPU power models on heterogeneous multi-cluster ARM-based mobile SoCs without additional hardware support or external power measurement tools.