mlsanityData to 7 Oct 2026

Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer

arXiv 2102.12357 ↗First posted 24 Feb 2021cs.IT cs.LG cs.NIcited by 10

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Abstract (arXiv)

Federated edge learning (FEEL) is a widely adopted framework for training an artificial intelligence (AI) model distributively at edge devices to leverage their data while preserving their data privacy. The execution of a power-hungry learning task at energy-constrained devices is a key challenge confronting the implementation of FEEL. To tackle the challenge, we propose the solution of powering devices using wireless power transfer (WPT). To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point). The development of the proposed analytical framework relates the accuracy of distributed stochastic gradient estimation to the WPT settings, the randomness in both communication and WPT links, and devices' computation capacities. Furthermore, the local-computation at devices (i.e., mini-batch size and processor clock frequency) is optimized to efficiently use the harvested energy for gradient estimation. The resultant learning-WPT tradeoffs reveal the simple scaling laws of the model-convergence rate with respect to the transferred energy as well as the devices' computational energy efficiencies. The results provide useful guidelines on WPT provisioning to provide a guaranteer on learning performance. They are corroborated by experimental results using a real dataset.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 24 Feb 2021.

Builds on

30 of 45 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  3. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  4. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 561
  5. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  7. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  8. 2017Stochastic Gradient Descent as Approximate Bayesian Inferencecited by 298
  9. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 277
  10. 2018An Empirical Model of Large-Batch Trainingcited by 268
  11. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  12. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
18 more
  1. 2019Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communicationcited by 218
  2. 2018Federated Learning via Over-the-Air Computationcited by 215
  3. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  4. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  5. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 179
  6. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  7. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  8. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  9. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 91
  10. 2017Natasha 2: Faster Non-Convex Optimization Than SGDcited by 49
  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. 2019High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learningcited by 32
  14. 2020Energy-Efficient Federated Edge Learning with Joint Communication and Computation Designcited by 30
  15. 2019Energy-Aware Analog Aggregation for Federated Learning with Redundant Datacited by 23
  16. 2020Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learningcited by 15
  17. 2018Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designscited by 5
  18. 2021Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensingcited by 2

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 3; 2025: 3.

10 papers.

  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  3. 2023Analysis and Optimization of Wireless Federated Learning with Data Heterogeneitycited by 3
  4. 2023Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHAcited by 2
  5. 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
  6. 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneitycited by 2
  7. 2024LoLaFL: Low-Latency Federated Learning via Forward-only Propagationcited by 1
  8. 2025A Generalized Hierarchical Federated Learning Framework with Theoretical Guaranteescited by 1
  9. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  10. 2024A Hierarchical Federated Learning Approach for the Internet of Thingscited by 0