mlsanityData to 14 Oct 2026

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

arXiv 2101.03627 ↗First posted 10 Jan 2021cs.NI cs.LGcited by 10

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Abstract (arXiv)

This paper studies a federated learning (FL) system, where textitmultiple FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising intra-service resource allocation and inter-service resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service owners as the bidders and the network provider as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 35 of its 46 references that are in the map. First posted on arXiv 10 Jan 2021.

Builds on

35 of 46 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2018Federated Learning with Non-IID Datacited by 1,108
  7. 2017Federated Multi-Task Learningcited by 672
  8. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  9. 2019Agnostic Federated Learningcited by 456
  10. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  11. 2019Federated Machine Learning: Concept and Applicationscited by 412
  12. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
23 more
  1. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  2. 2019Fair Resource Allocation in Federated Learningcited by 394
  3. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  4. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  5. 2017Sparse Communication for Distributed Gradient Descentcited by 249
  6. 2018Federated Learning via Over-the-Air Computationcited by 219
  7. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  8. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  9. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 175
  10. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
  11. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 153
  12. 2018Blockchained On-Device Federated Learningcited by 106
  13. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 103
  14. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 96
  15. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  16. 2018Wireless Network Intelligence at the Edgecited by 92
  17. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  18. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  19. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  20. 2019Motivating Workers in Federated Learning: A Stackelberg Game Perspectivecited by 45
  21. 2019Energy-Efficient Radio Resource Allocation for Federated Edge Learningcited by 39
  22. 2018Joint Service Pricing and Cooperative Relay Communication for Federated Learningcited by 22
  23. 2020Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networkscited by 11

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2; 2025: 3.

10 papers.

  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  2. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  3. 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devicescited by 10
  4. 2024Intelligent Agents for Auction-based Federated Learning: A Surveycited by 4
  5. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  6. 2022Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgetscited by 2
  7. 2025Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneitycited by 2
  8. 2024DynamicFL: Federated Learning with Dynamic Communication Resource Allocationcited by 1
  9. 2025Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Servicescited by 1
  10. 2025CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesterscited by 0