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Cost-Effective Federated Learning in Mobile Edge Networks

arXiv 2109.05411 ↗First posted 12 Sep 2021cs.LG cs.DC cs.NIcited by 25

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Abstract (arXiv)

Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 31 of its 46 references that are in the map. First posted on arXiv 12 Sep 2021.

Builds on

31 of 46 references.

References · 31

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2020Adaptive Federated Optimizationcited by 1,036
  7. 2019Towards Federated Learning at Scale: System Designcited by 826
  8. 2017Federated Multi-Task Learningcited by 669
  9. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  10. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
19 more
  1. 2018Federated Learning via Over-the-Air Computationcited by 215
  2. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  3. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  4. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  5. 2018Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithmscited by 106
  6. 2018Wireless Network Intelligence at the Edgecited by 90
  7. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  8. 2019First Analysis of Local GD on Heterogeneous Datacited by 80
  9. 2019Variance Reduced Local SGD with Lower Communication Complexitycited by 80
  10. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  11. 2018Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGDcited by 76
  12. 2019Accelerating Federated Learning via Momentum Gradient Descentcited by 75
  13. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  14. 2020Fast-Convergent Federated Learningcited by 61
  15. 2020Cost-Effective Federated Learning Designcited by 61
  16. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  17. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  18. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  19. 2020Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocationcited by 18

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Citing papers per year: 2021: 2; 2022: 2; 2023: 10; 2024: 10; 2025: 1.

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  1. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  2. 2023Why Batch Normalization Damage Federated Learning on Non-IID Data?cited by 29
  3. 2022FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregationcited by 21
  4. 2023FedDefender: Client-Side Attack-Tolerant Federated Learningcited by 12
  5. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  6. 2021Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoTcited by 9
  7. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
  8. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  9. 2023Handling Data Heterogeneity via Architectural Design for Federated Visual Recognitioncited by 8
  10. 2023Federated Learning While Providing Model as a Service: Joint Training and Inference Optimizationcited by 8
  11. 2023Optimization Design for Federated Learning in Heterogeneous 6G Networkscited by 7
  12. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
13 more
  1. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  2. 2024Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learningcited by 5
  3. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  4. 2024Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analyticscited by 3
  5. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  6. 2023Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Controlcited by 1
  7. 2024Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Samplingcited by 1
  8. 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networkscited by 1
  9. 2024Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Designcited by 1
  10. 2025Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Samplingcited by 1
  11. 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learningcited by 0
  12. 2024Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learningcited by 0
  13. 2024Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learningcited by 0