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Efficient Federated Meta-Learning over Multi-Access Wireless Networks

arXiv 2108.06453 ↗First posted 14 Aug 2021cs.LGcited by 13

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Abstract (arXiv)

Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its performance is often limited by slow convergence and corresponding low communication efficiency. In addition, since the available radio spectrum and IoT devices' energy capacity are usually insufficient, it is crucial to control the resource allocation and energy consumption when deploying FML in practical wireless networks. To overcome the challenges, in this paper, we rigorously analyze each device's contribution to the global loss reduction in each round and develop an FML algorithm (called NUFM) with a non-uniform device selection scheme to accelerate the convergence. After that, we formulate a resource allocation problem integrating NUFM in multi-access wireless systems to jointly improve the convergence rate and minimize the wall-clock time along with energy cost. By deconstructing the original problem step by step, we devise a joint device selection and resource allocation strategy to solve the problem with theoretical guarantees. Further, we show that the computational complexity of NUFM can be reduced from O(d2) to O(d) (with the model dimension d) via combining two first-order approximation techniques. Extensive simulation results demonstrate the effectiveness and superiority of the proposed methods in comparison with existing baselines.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 33 of its 48 references that are in the map. First posted on arXiv 14 Aug 2021.

Builds on

33 of 48 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  8. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  9. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  10. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 228
  11. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  12. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
21 more
  1. 2019On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimizationcited by 180
  2. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 155
  3. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 144
  4. 2019On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithmscited by 132
  5. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  6. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 94
  7. 2018Wireless Network Intelligence at the Edgecited by 92
  8. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  9. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  10. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  11. 2020Cost-Effective Federated Learning Designcited by 62
  12. 2020Fast-Convergent Federated Learningcited by 61
  13. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  14. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  15. 2019Differentially Private Meta-Learningcited by 41
  16. 2019Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entitiescited by 41
  17. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  18. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34
  19. 2020Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learningcited by 22
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  21. 2021TODG: Distributed Task Offloading with Delay Guarantees for Edge Computingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 2; 2024: 3; 2025: 2; 2026: 1.

13 papers.

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  1. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  2. 2023LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platformscited by 11
  3. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  4. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
  5. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  6. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  7. 2022Federated Meta-Learning for Traffic Steering in O-RANcited by 4
  8. 2024Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offscited by 3
  9. 2024A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Frameworkcited by 3
  10. 2022Fed-TDA: Federated Tabular Data Augmentation on Non-IID Datacited by 1
  11. 2025Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systemscited by 0
  12. 2025Meta-Learning-Based Handover Management in NextG O-RANcited by 0
1 more
  1. 2026Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Schedulingcited by 0