mlsanityData to 14 Oct 2026

Wireless Quantized Federated Learning: A Joint Computation and Communication Design

arXiv 2203.05878 ↗First posted 11 Mar 2022eess.SP cs.LGcited by 15

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Abstract (arXiv)

Recently, federated learning (FL) has sparked widespread attention as a promising decentralized machine learning approach which provides privacy and low delay. However, communication bottleneck still constitutes an issue, that needs to be resolved for an efficient deployment of FL over wireless networks. In this paper, we aim to minimize the total convergence time of FL, by quantizing the local model parameters prior to uplink transmission. More specifically, the convergence analysis of the FL algorithm with stochastic quantization is firstly presented, which reveals the impact of the quantization error on the convergence rate. Following that, we jointly optimize the computing, communication resources and number of quantization bits, in order to guarantee minimized convergence time across all global rounds, subject to energy and quantization error requirements, which stem from the convergence analysis. The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed. Moreover, the proposed method is shown to result in faster convergence in comparison with baseline schemes. Finally, useful insights for the selection of the quantization error tolerance are provided.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 30 of its 47 references that are in the map. First posted on arXiv 11 Mar 2022.

Builds on

30 of 47 references.

References · 30

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2021Convex optimizationcited by 2,515
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  7. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  9. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  10. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  11. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  12. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 163
18 more
  1. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 153
  2. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  3. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  4. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  5. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  6. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  7. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  8. 2020Federated Learning With Quantized Global Model Updatescited by 55
  9. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  10. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  11. 2018Application of Machine Learning in Wireless Networks: Key Techniques and Open Issuescited by 30
  12. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  13. 2019Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learningcited by 17
  14. 2020Communication Efficient Federated Learning over Multiple Access Channelscited by 11
  15. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  16. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigmcited by 6
  17. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trendscited by 6
  18. 2021Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challengescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 5; 2025: 4; 2026: 3.

15 papers.

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  1. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  2. 2024Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimizationcited by 13
  3. 2023Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updatescited by 8
  4. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
  5. 2024FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Modelscited by 5
  6. 2025Biased Federated Learning under Wireless Heterogeneitycited by 4
  7. 2023Network Adaptive Federated Learning: Congestion and Lossy Compressioncited by 3
  8. 2024Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsificationcited by 3
  9. 2025Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networkscited by 2
  10. 2024Towards Federated Learning with On-device Training and Communication in 8-bit Floating Pointcited by 1
  11. 2025Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learningcited by 1
  12. 2026Decentralized Federated Learning With Energy Harvesting Devicescited by 1
3 more
  1. 2025Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Schedulingcited by 0
  2. 2026Quantization in Federated Learning: Methods, Challenges and Future Directionscited by 0
  3. 2026Polar Code Based Federated Learning: Convergence Analysis and Resource Allocationcited by 0

Similar, not cited

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  1. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  2. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
  3. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
  4. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  5. 2024FedFQ: Federated Learning with Fine-Grained Quantization
  6. 2025Lightweight Federated Learning over Wireless Edge Networks
  7. 2024Rate-Constrained Quantization for Communication-Efficient Federated Learning
  8. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization