mlsanityData to 14 Oct 2026

Joint Optimization of Energy Consumption and Completion Time in Federated Learning

arXiv 2209.14900 ↗First posted 29 Sep 2022cs.LG eess.SPcited by 5

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Abstract (arXiv)

Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 80 references that are in the map. First posted on arXiv 29 Sep 2022.

Builds on

34 of 80 references.

References · 34

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2021Convex optimizationcited by 2,515
  3. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  5. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  7. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  8. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  9. 2020FetchSGD: Communication-Efficient Federated Learning with Sketchingcited by 179
  10. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  11. 2020Distilled One-Shot Federated Learningcited by 109
  12. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
22 more
  1. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 94
  2. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  3. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 81
  4. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  5. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  6. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  7. 2020Cost-Effective Federated Learning Designcited by 62
  8. 2020FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MECcited by 48
  9. 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 46
  10. 2019Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques and Toolscited by 40
  11. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  12. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34
  13. 2020Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarmscited by 31
  14. 2020Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneitycited by 20
  15. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  16. 2020Delay Minimization for Federated Learning Over Wireless Communication Networkscited by 12
  17. 2021Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approachcited by 11
  18. 2021Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updatescited by 10
  19. 2021The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprintingcited by 3
  20. 2021CFLMEC: Cooperative Federated Learning for Mobile Edge Computingcited by 1
  21. 2021From Griefing to Stability in Blockchain Mining Economiescited by 1
  22. 2021A Context-aware Radio Resource Management in Heterogeneous Virtual RANscited by 1

Built on by

Cited by year

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

5 papers.

  1. 2022Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Realitycited by 9
  2. 2022Time Minimization in Hierarchical Federated Learningcited by 4
  3. 2024Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Schemecited by 3
  4. 2023A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directionscited by 2
  5. 2023Resource Allocation for Semantic Communication under Physical-layer Securitycited by 0