mlsanityData to 7 Oct 2026

On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks

arXiv 2111.07911 ↗First posted 15 Nov 2021cs.LG cs.NIcited by 10

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Abstract (arXiv)

Deploying federated learning (FL) over wireless networks with resource-constrained devices requires balancing between accuracy, energy efficiency, and precision. Prior art on FL often requires devices to train deep neural networks (DNNs) using a 32-bit precision level for data representation to improve accuracy. However, such algorithms are impractical for resource-constrained devices since DNNs could require execution of millions of operations. Thus, training DNNs with a high precision level incurs a high energy cost for FL. In this paper, a quantized FL framework, that represents data with a finite level of precision in both local training and uplink transmission, is proposed. Here, the finite level of precision is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each device trains its QNN and transmits a quantized training result to the base station. Energy models for the local training and the transmission with the quantization are rigorously derived. An energy minimization problem is formulated with respect to the level of precision while ensuring convergence. To solve the problem, we first analytically derive the FL convergence rate and use a line search method. Simulation results show that our FL framework can reduce energy consumption by up to 53% compared to a standard FL model. The results also shed light on the tradeoff between precision, energy, and accuracy in FL over wireless networks.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 14 of its 17 references that are in the map. First posted on arXiv 15 Nov 2021.

Builds on

14 of 17 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2019Green AIcited by 620
  5. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 413
  6. 2015Deep Learning with Limited Numerical Precisioncited by 368
  7. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  8. 2018SGD and Hogwild! Convergence Without the Bounded Gradients Assumptioncited by 94
  9. 2020Cost-Effective Federated Learning Designcited by 61
  10. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  11. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  12. 2017Minimum Energy Quantized Neural Networkscited by 20
2 more
  1. 2021Communication-Efficient Federated Learning with Binary Neural Networkscited by 13
  2. 2021A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 0; 2025: 3; 2026: 1.

10 papers.

  1. 2023A Systematic Review of Green AIcited by 77
  2. 2023A Synthesis of Green Architectural Tactics for ML-Enabled Systemscited by 17
  3. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  4. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  5. 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 9
  6. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  7. 2025Choosing to Be Green: Advancing Green AI via Dynamic Model Selectioncited by 2
  8. 2025SustainDiffusion: Optimising the Social and Environmental Sustainability of Stable Diffusion Modelscited by 0
  9. 2025Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devicescited by 0
  10. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0