mlsanityData to 7 Oct 2026

Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

arXiv 2003.00199 ↗First posted 29 Feb 2020cs.IT eess.SP math.ITcited by 30

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Abstract (arXiv)

This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML parameters aggregation and the computation resource allocation for locally updating MLparameters are jointly optimized. In particular, we consider two transmission protocols for edge devices to upload ML parameters to edge server, based on the non orthogonal multiple access and time division multiple access, respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading MLparameters and their central processing unit frequencies for local update. We propose efficient algorithms to optimally solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes, our proposed joint communication and computation design significantly improves the energy efficiency of the federated edge learning system, by properly balancing the energy tradeoff between communication and computation.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Feb 2020.

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Citing papers per year: 2021: 10; 2022: 1; 2023: 7; 2024: 7; 2025: 4; 2026: 1.

30 papers.

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  1. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  2. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  3. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  4. 2023Green Edge AI: A Contemporary Surveycited by 24
  5. 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
  6. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  7. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
  8. 2021Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysiscited by 13
  9. 2024Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigmcited by 13
  10. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
  11. 2021Federated Learning over Noisy Channels: Convergence Analysis and Design Examplescited by 8
  12. 2021Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learningcited by 7
18 more
  1. 2022Energy-Aware Edge Association for Cluster-based Personalized Federated Learningcited by 6
  2. 2024Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approachcited by 6
  3. 2025AI Governance through Marketscited by 5
  4. 2024Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Managementcited by 4
  5. 2021Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networkscited by 3
  6. 2021Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learningcited by 2
  7. 2023Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSIcited by 2
  8. 2024Robust Communication and Computation using Deep Learning via Joint Uncertainty Injectioncited by 2
  9. 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
  10. 2025Hierarchical Federated Learning for Social Network with Mobilitycited by 2
  11. 2021Energy-Efficient Multi-Orchestrator Mobile Edge Learningcited by 1
  12. 2023A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networkscited by 1
  13. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  14. 2025Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learningcited by 1
  15. 2026Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluationcited by 1
  16. 2023Federated Learning for Energy Constrained IoT devices: A systematic mapping studycited by 0
  17. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  18. 2024Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devicescited by 0