# Machine Intelligence at the Edge with Learning Centric Power Allocation

arXiv: https://arxiv.org/abs/1911.04922 · HTML: https://mlsanity.com/paper/1911.04922/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Edge Federated Learning Via Unit-Modulus Over-The-Air Computation](https://mlsanity.com/paper/2101.12051/) (2021)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices](https://mlsanity.com/paper/2310.13981/) (2023)
- [Accelerating Edge Intelligence via Integrated Sensing and Communication](https://mlsanity.com/paper/2107.09574/) (2021)
- [Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach](https://mlsanity.com/paper/2107.09621/) (2021)
- [LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication](https://mlsanity.com/paper/2401.02675/) (2024)
- [STT-GS: Sample-Then-Transmit Edge Gaussian Splatting with Joint Client Selection and Power Control](https://mlsanity.com/paper/2510.13186/) (2025)
- [Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication](https://mlsanity.com/paper/2510.22718/) (2025)
- [Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication](https://mlsanity.com/paper/2311.12223/) (2023)
- [A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning](https://mlsanity.com/paper/2509.18120/) (2025)
- [A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control](https://mlsanity.com/paper/2504.01752/) (2025)
- [Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks](https://mlsanity.com/paper/2604.10662/) (2026)
- [Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning](https://mlsanity.com/paper/2604.14886/) (2026)

## Abstract (arXiv)

While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.