mlsanityData to 7 Oct 2026

Accelerating DNN Training in Wireless Federated Edge Learning Systems

arXiv 1905.09712 ↗First posted 23 May 2019cs.LG eess.SPcited by 34

Not on the map. Why?

Abstract (arXiv)

Training task in classical machine learning models, such as deep neural networks, is generally implemented at a remote cloud center for centralized learning, which is typically time-consuming and resource-hungry. It also incurs serious privacy issue and long communication latency since a large amount of data are transmitted to the centralized node. To overcome these shortcomings, we consider a newly-emerged framework, namely federated edge learning, to aggregate local learning updates at the network edge in lieu of users' raw data. Aiming at accelerating the training process, we first define a novel performance evaluation criterion, called learning efficiency. We then formulate a training acceleration optimization problem in the CPU scenario, where each user device is equipped with CPU. The closed-form expressions for joint batchsize selection and communication resource allocation are developed and some insightful results are highlighted. Further, we extend our learning framework to the GPU scenario. The optimal solution in this scenario is manifested to have the similar structure as that of the CPU scenario, recommending that our proposed algorithm is applicable in more general systems. Finally, extensive experiments validate the theoretical analysis and demonstrate that the proposed algorithm can reduce the training time and improve the learning accuracy simultaneously.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 10; 2023: 8; 2024: 2; 2025: 1; 2026: 2.

34 papers.

Sort:

  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
  3. 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
  4. 2021Decentralized Inference with Graph Neural Networks in Wireless Communication Systemscited by 18
  5. 2022Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networkingcited by 18
  6. 2022Trustworthy Federated Learning via Blockchaincited 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. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  10. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  11. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  12. 2022Multi-user Co-inference with Batch Processing Capable Edge Servercited by 11
22 more
  1. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
  2. 2021CSAFL: A Clustered Semi-Asynchronous Federated Learning Frameworkcited by 10
  3. 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
  4. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  5. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
  6. 2022Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspectivecited by 8
  7. 2022Federated Learning with Erroneous Communication Linkscited by 5
  8. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  9. 2022Personalized Federated Learning with Exact Stochastic Gradient Descentcited by 4
  10. 2022Client Selection for Federated Bayesian Learningcited by 4
  11. 2023Adaptive Federated Learning via New Entropy Approachcited by 3
  12. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  13. 2024Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocationcited by 3
  14. 2021Adaptive Scheduling for Machine Learning Tasks over Networkscited by 2
  15. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  16. 2023Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networkscited by 2
  17. 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneitycited by 2
  18. 2023Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysiscited by 1
  19. 2023Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learningcited by 1
  20. 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devicescited by 0
  21. 2026Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networkscited by 0
  22. 2026Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networkscited by 0