mlsanityData to 14 Oct 2026

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization

arXiv 2402.12957 ↗First posted 20 Feb 2024cs.DCcited by 1

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Abstract (arXiv)

Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. However, FL in wireless networks still faces two major challenges, i.e., large communication overhead and high energy consumption, which are exacerbated by client heterogeneity in dataset sizes and wireless channels. While model quantization is effective for energy reduction, existing works ignore adapting quantization to heterogeneous clients and FL convergence. To address these challenges, this paper develops an energy optimization problem of jointly designing quantization levels, scheduling clients, allocating channels, and controlling computation frequencies (QCCF) in wireless FL. Specifically, we derive an upper bound identifying the influence of client scheduling and quantization errors on FL convergence. Under the longterm convergence constraints and wireless constraints, the problem is established and transformed into an instantaneous problem with Lyapunov optimization. Solving Karush-Kuhn-Tucker conditions, our closed-form solution indicates that the doubly adaptive quantization level rises with the training process and correlates negatively with dataset sizes. Experiment results validate our theoretical results, showing that QCCF consumes less energy with faster convergence compared with state-of-the-art baselines.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 52 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

24 of 52 references.

References · 24

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  3. 2018LEAF: A Benchmark for Federated Settingscited by 792
  4. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  5. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  6. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
  7. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 153
  8. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  9. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 63
  10. 2020An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guaranteecited by 62
  11. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 56
  12. 2020Federated Learning With Quantized Global Model Updatescited by 55
12 more
  1. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 41
  2. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 25
  3. 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 23
  4. 2018Double Quantization for Communication-Efficient Distributed Optimizationcited by 19
  5. 2021Communication-Efficient Federated Learning with Binary Neural Networkscited by 14
  6. 2022Low-latency Federated Learning with DNN Partition in Distributed Industrial IoT Networkscited by 14
  7. 2021Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Schedulingcited by 12
  8. 2021Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Schedulingcited by 8
  9. 2020Communication Efficient Federated Learning with Energy Awareness over Wireless Networkscited by 7
  10. 2021Communication Efficient Federated Learning with Adaptive Quantizationcited by 4
  11. 2023Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learningcited by 3
  12. 2020Joint Optimization of User Association, Subchannel Allocation, and Power Allocation in Multi-cell Multi-association OFDMA Heterogeneous Networkscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learningcited by 0

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  1. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  2. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  3. 2024Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization
  4. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
  5. 2025Lightweight Federated Learning over Wireless Edge Networks
  6. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
  7. 2021Quantized Federated Learning under Transmission Delay and Outage Constraints
  8. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions