mlsanityData to 7 Oct 2026

Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing

arXiv 2007.07122 ↗First posted 14 Jul 2020cs.IT eess.SP math.ITcited by 39

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Abstract (arXiv)

Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intelligence (AI) models. Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation. FEEL coordinates global model training at an edge server and local model training at edge devices that are connected by wireless links. This work contributes to the energy-efficient implementation of FEEL in wireless networks by designing joint computation-and-communication resource management (C2RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both a CPU and a GPU, called heterogeneous computing, can significantly improve both the performance and energy efficiency. To minimize the sum energy consumption of devices, we propose a novel C2RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and C2 time division for each link. The key component of the framework is a set of equilibriums in energy rates with respect to different control variables that are proved to exist among devices or between processing units at each device. The results are applied to designing efficient algorithms for computing the optimal C2RM policies faster than the standard optimization tools. Based on the equilibriums, we further design energy-efficient schemes for device scheduling and greedy spectrum sharing that scavenges "spectrum holes" resulting from heterogeneous C2 time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of C2RM on improving the energy efficiency of a FEEL system.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jul 2020.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 6; 2024: 7; 2025: 9; 2026: 3.

39 papers.

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  1. 2024Fast On-device LLM Inference with NPUscited by 58
  2. 2021Towards Fairness-Aware Federated Learningcited by 57
  3. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  4. 2023Green Edge AI: A Contemporary Surveycited by 24
  5. 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
  6. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  7. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  8. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  9. 2021Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysiscited by 13
  10. 2024Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigmcited by 13
  11. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  12. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
27 more
  1. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  2. 2025DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysiscited by 9
  3. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
  4. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 7
  5. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  6. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  7. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  8. 2023Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacycited by 5
  9. 2025Revisiting Outage for Edge Inference Systemscited by 5
  10. 2025Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learningcited by 5
  11. 2021Collaborative Learning over Wireless Networks: An Introductory Overviewcited by 4
  12. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  13. 2022ModularFed: Leveraging Modularity in Federated Learning Frameworkscited by 4
  14. 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
  15. 2024Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Managementcited by 4
  16. 2024Adaptive Split Learning over Energy-Constrained Wireless Edge Networkscited by 4
  17. 2025Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Timecited by 4
  18. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  19. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  20. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  21. 2025Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Seriescited by 1
  22. 2025A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Controlcited by 0
  23. 2025WindVE: Collaborative CPU-NPU Vector Embeddingcited by 0
  24. 2025Multi-Timescale Primal Dual Hybrid Gradient with Application to Distributed Optimizationcited by 0
  25. 2026Hardware-Aware Federated Learning for Speech Emotion Recognitioncited by 0
  26. 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networkscited by 0
  27. 2026Efficient Resource Optimization for Split Federated Learningcited by 0