AI RESEARCH
A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices
arXiv CS.LG
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ArXi:2605.27601v1 Announce Type: cross 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 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.