mlsanityData to 7 Oct 2026

Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission

arXiv 2012.11070 ↗First posted 21 Dec 2020cs.NI cs.LGcited by 22

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Abstract (arXiv)

Federated learning (FL) is a popular collaborative distributed machine learning paradigm across mobile devices. However, practical FL over resource constrained mobile devices confronts multiple challenges, e.g., the local on-device training and model updates in FL are power hungry and radio resource intensive for mobile devices. To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices. Specifically, we develop flexible weight quantization schemes to facilitate on-device local training over heterogeneous mobile devices. Based on the observation that the energy consumption of local computing is comparable to that of model updates, we formulate the energy efficient FL problem into a mixed-integer programming problem where the quantization and spectrum resource allocation strategies are jointly determined for heterogeneous mobile devices to minimize the overall FL energy consumption (computation + transmissions) while guaranteeing model performance and training latency. Since the optimization variables of the problem are strongly coupled, an efficient iterative algorithm is proposed, where the bandwidth allocation and weight quantization levels are derived. Extensive simulations are conducted to verify the effectiveness of the proposed scheme.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 6; 2025: 7; 2026: 2.

22 papers.

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  1. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  2. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devicescited by 16
  3. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  4. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  5. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 10
  6. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  7. 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
  8. 2023FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Modelscited by 4
  9. 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
  10. 2025NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNNcited by 4
  11. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  12. 2025Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learningcited by 3
10 more
  1. 2025Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environmentcited by 3
  2. 2024On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networkscited by 2
  3. 2025Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correctioncited by 2
  4. 2023Over-the-Air Federated Averaging with Limited Power and Privacy Budgetscited by 1
  5. 2024FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shiftingcited by 0
  6. 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Controlcited by 0
  7. 2025Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Designcited by 0
  8. 2025Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devicescited by 0
  9. 2026Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregationcited by 0
  10. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networkscited by 0