mlsanityData to 7 Oct 2026

Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design

arXiv 2207.09387 ↗First posted 19 Jul 2022cs.LGcited by 15

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Abstract (arXiv)

In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed. Here, the finite precision level 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 quantization are rigorously derived. To minimize the energy consumption and the number of communication rounds simultaneously, a multi-objective optimization problem is formulated with respect to the number of local iterations, the number of selected devices, and the precision levels for both local training and transmission while ensuring convergence under a target accuracy constraint. To solve this problem, the convergence rate of the proposed FL system is analytically derived with respect to the system control variables. Then, the Pareto boundary of the problem is characterized to provide efficient solutions using the normal boundary inspection method. Design insights on balancing the tradeoff between the two objectives while achieving a target accuracy are drawn from using the Nash bargaining solution and analyzing the derived convergence rate. Simulation results show that the proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm that represents data with full precision without damaging the convergence rate.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 29 of its 53 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

29 of 53 references.

References · 29

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2021Convex optimizationcited by 2,479
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2019Green AIcited by 620
  5. 2016DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradientscited by 557
  6. 2017Opening the Black Box of Deep Neural Networks via Informationcited by 494
  7. 2019Rigging the Lottery: Making All Tickets Winnerscited by 491
  8. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 413
  9. 2015Deep Learning with Limited Numerical Precisioncited by 368
  10. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  11. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  12. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
17 more
  1. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  2. 2018SGD and Hogwild! Convergence Without the Bounded Gradients Assumptioncited by 94
  3. 2020Cost-Effective Federated Learning Designcited by 61
  4. 2020Ternary Compression for Communication-Efficient Federated Learningcited by 57
  5. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  6. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  7. 2015Variance Reduced Stochastic Gradient Descent with Neighborscited by 39
  8. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  9. 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 22
  10. 2017Minimum Energy Quantized Neural Networkscited by 20
  11. 2020The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descentcited by 16
  12. 2022An Energy and Carbon Footprint Analysis of Distributed and Federated Learningcited by 16
  13. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  14. 2021Communication-Efficient Federated Learning with Binary Neural Networkscited by 13
  15. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  16. 2014Multi-Objective Signal Processing Optimization: The Way to Balance Conflicting Metrics in 5G Systemscited by 4
  17. 2021Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approachcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 4; 2026: 2.

15 papers.

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  1. 2023Green Edge AI: A Contemporary Surveycited by 24
  2. 2024Federated Learning Priorities Under the European Union Artificial Intelligence Actcited by 13
  3. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  4. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
  5. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networkscited by 2
  6. 2024SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farmscited by 1
  7. 2024Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrivalcited by 1
  8. 2024Communication and Energy Efficient Federated Learning using Zero-Order Optimization Techniquecited by 1
  9. 2025Deploying Large AI Models on Resource-Limited Devices with Split Federated Learningcited by 1
  10. 2025Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Servicescited by 1
  11. 2023Transformers for Green Semantic Communication: Less Energy, More Semanticscited by 0
  12. 2024MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communicationcited by 0
3 more
  1. 2025FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learningcited by 0
  2. 2026Standardized Methods and Recommendations for Green Federated Learningcited by 0
  3. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networkscited by 0