mlsanityData to 7 Oct 2026

Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks

arXiv 2206.02398 ↗First posted 6 Jun 2022eess.SP cs.IT cs.LGcited by 15

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Abstract (arXiv)

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 33 of its 55 references that are in the map. First posted on arXiv 6 Jun 2022.

Builds on

33 of 55 references.

References · 33

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  7. 2018Federated Learning via Over-the-Air Computationcited by 215
  8. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  9. 2019Federated Learning over Wireless Fading Channelscited by 178
  10. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  11. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  12. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
21 more
  1. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  2. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  3. 2019Reliable Federated Learning for Mobile Networkscited by 92
  4. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  5. 2020HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learningcited by 74
  6. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  7. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  8. 2022Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learningcited by 59
  9. 2017Natasha 2: Faster Non-Convex Optimization Than SGDcited by 49
  10. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 45
  11. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  12. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 32
  13. 2020Convergence of Federated Learning over a Noisy Downlinkcited by 29
  14. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  15. 2019Artificial Intelligence-Enabled Intelligent 6G Networkscited by 27
  16. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
  17. 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
  18. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  19. 2021Over-the-Air Computation via Reconfigurable Intelligent Surfacecited by 12
  20. 2021Relay-Assisted Cooperative Federated Learningcited by 9
  21. 2019Time Synchronization in 5G Wireless Edge: Requirements and Solutions for Critical-MTCcited by 7

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Cited by year

Citing papers per year: 2022: 3; 2023: 8; 2024: 3; 2025: 1.

15 papers.

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  1. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  2. 2022A Survey on Over-the-Air Computationcited by 19
  3. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  4. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  5. 2024A Survey on Integrated Sensing, Communication, and Computationcited by 12
  6. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  7. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  8. 2024Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneitycited by 6
  9. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3
  10. 2022On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stationscited by 2
  11. 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
  12. 2023Towards Scalable Wireless Federated Learning: Challenges and Solutionscited by 1
3 more
  1. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  2. 2024Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systemscited by 1
  3. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0