mlsanityData to 14 Oct 2026

Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization

arXiv 2412.20785 ↗First posted 30 Dec 2024cs.LGcited by 1

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Abstract (arXiv)

Federated Learning (FL) enables clients to share learning parameters instead of local data, reducing communication overhead. Traditional wireless networks face latency challenges with FL. In contrast, Cell-Free Massive MIMO (CFmMIMO) can serve multiple clients on shared resources, boosting spectral efficiency and reducing latency for large-scale FL. However, clients' communication resource limitations can hinder the completion of the FL training. To address this challenge, we propose an energy-efficient, low-latency FL framework featuring optimized uplink power allocation for seamless client-server collaboration. Our framework employs an adaptive quantization scheme, dynamically adjusting bit allocation for local gradient updates to reduce communication costs. We formulate a joint optimization problem covering FL model updates, local iterations, and power allocation, solved using sequential quadratic programming (SQP) to balance energy and latency. Additionally, clients use the AdaDelta method for local FL model updates, enhancing local model convergence compared to standard SGD, and we provide a comprehensive analysis of FL convergence with AdaDelta local updates. Numerical results show that, within the same energy and latency budgets, our power allocation scheme outperforms the Dinkelbach and max-sum rate methods by increasing the test accuracy up to 7% and 19%, respectively. Moreover, for the three power allocation methods, our proposed quantization scheme outperforms AQUILA and LAQ by increasing test accuracy by up to 36% and 35%, respectively.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 30 Dec 2024.

Builds on

23 of 36 references.

References · 23

  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  3. 2020Adaptive Federated Optimizationcited by 1,046
  4. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  5. 2021Decentralized Federated Averagingcited by 114
  6. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 63
  7. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  8. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 41
  9. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  10. 2023Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedupcited by 23
  11. 2021FedDQ: Communication-Efficient Federated Learning with Descending Quantizationcited by 20
  12. 2021Foundations of User-Centric Cell-Free Massive MIMOcited by 17
11 more
  1. 2022SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communicationscited by 11
  2. 2023More Communication Does Not Result in Smaller Generalization Error in Federated Learningcited by 10
  3. 2023AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategycited by 7
  4. 2015Energy-Efficient Power Control: A Look at 5G Wireless Technologiescited by 6
  5. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantizationcited by 5
  6. 2022FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learningcited by 4
  7. 2022A-LAQ: Adaptive Lazily Aggregated Quantized Gradientcited by 3
  8. 2023Joint Compression and Deadline Optimization for Wireless Federated Learningcited by 3
  9. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  10. 2024Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networkscited by 2
  11. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networkscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learningcited by 0

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  3. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
  4. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  5. 2021Quantized Federated Learning under Transmission Delay and Outage Constraints
  6. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  7. 2023Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks
  8. 2025Lightweight Federated Learning over Wireless Edge Networks