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To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

arXiv 2012.11804 ↗First posted 22 Dec 2020cs.LG cs.AIcited by 45

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Abstract (arXiv)

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2020.

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Citing papers per year: 2021: 6; 2022: 13; 2023: 6; 2024: 11; 2025: 7; 2026: 2.

45 papers.

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  1. 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29
  2. 2023Green Edge AI: A Contemporary Surveycited by 24
  3. 2022Multi-Job Intelligent Scheduling with Cross-Device Federated Learningcited by 20
  4. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  5. 2021Towards Energy Efficient Federated Learning over 5G+ Mobile Devicescited by 16
  6. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  7. 2024Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigmcited by 13
  8. 2022Federated Learning with Flexible Controlcited by 12
  9. 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10
  10. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  11. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  12. 2025MobiLLM: Enabling LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuningcited by 9
33 more
  1. 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
  2. 2023Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Frameworkcited by 6
  3. 2022Towards Communication-Learning Trade-off for Federated Learning at the Network Edgecited by 5
  4. 2022A Fast Blockchain-based Federated Learning Framework with Compressed Communicationscited by 5
  5. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  6. 2022Social-Aware Clustered Federated Learning with Customized Privacy Preservationcited by 5
  7. 2021FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computingcited by 4
  8. 2022Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learningcited by 3
  9. 2024Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networkscited by 3
  10. 2025PAE MobiLLM: Privacy-Aware and Efficient LLM Fine-Tuning on the Mobile Device via Additive Side-Tuningcited by 3
  11. 2021Management of Resource at the Network Edge for Federated Learningcited by 2
  12. 2021To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devicescited by 2
  13. 2024Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compressioncited by 2
  14. 2021OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learningcited by 1
  15. 2022Communication Efficient Distributed Learning over Wireless Channelscited by 1
  16. 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1
  17. 2024Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codescited by 1
  18. 2024SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farmscited by 1
  19. 2024Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devicescited by 1
  20. 2025ACME: Adaptive Customization of Large Models via Distributed Systemscited by 1
  21. 2023Federated Learning for Energy Constrained IoT devices: A systematic mapping studycited by 0
  22. 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devicescited by 0
  23. 2024WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Schedulingcited by 0
  24. 2024Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Clientcited by 0
  25. 2024FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selectioncited by 0
  26. 2024Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Networkcited by 0
  27. 2024BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTcited by 0
  28. 2025THOR: A Generic Energy Estimation Approach for On-Device Trainingcited by 0
  29. 2025Lightweight Federated Learning over Wireless Edge Networkscited by 0
  30. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  31. 2025Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Designcited by 0
  32. 2026SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studiescited by 0
  33. 2026MUC-FL: Block-Wise Marginal Utility Contribution for Communication-Efficient Federated Learningcited by 0