mlsanityData to 14 Oct 2026

Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation

arXiv 2208.07237 ↗First posted 15 Aug 2022cs.LG cs.AIcited by 6

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Abstract (arXiv)

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 23 of its 46 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

23 of 46 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Sparsified SGD with Memorycited by 376
  3. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  4. 2018The Convergence of Sparsified Gradient Methodscited by 229
  5. 2018Federated Learning via Over-the-Air Computationcited by 219
  6. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  7. 2019Federated Learning over Wireless Fading Channelscited by 183
  8. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  9. 2020Federated Learning with Compression: Unified Analysis and Sharp Guaranteescited by 160
  10. 2016Distributed Mean Estimation with Limited Communicationcited by 136
  11. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  12. 2018Communication Compression for Decentralized Trainingcited by 88
11 more
  1. 2019Accelerating Federated Learning via Momentum Gradient Descentcited by 76
  2. 2019Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedbackcited by 50
  3. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 46
  4. 2019Robust Federated Learning with Noisy Communicationcited by 34
  5. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 33
  6. 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 23
  7. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devicescited by 16
  8. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
  9. 2016Parallel and Distributed Methods for Nonconvex Optimization--Part II: Applicationscited by 10
  10. 2022Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learningcited by 8
  11. 2013An Overview on 3GPP Device-to-Device Proximity Servicescited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.

6 papers.

  1. 2022A Survey on Over-the-Air Computationcited by 21
  2. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  3. 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
  4. 2024Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysiscited by 0
  5. 2024How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?cited by 0
  6. 2025Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Designcited by 0