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Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach

arXiv 2011.10282 ↗First posted 20 Nov 2020cs.IT cs.LG cs.NIcited by 32

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Abstract (arXiv)

To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high communication latency and privacy issues as compared to centralized ML. To improve the communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a unified communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Nov 2020.

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Citing papers per year: 2021: 8; 2022: 9; 2023: 8; 2024: 3; 2025: 3; 2026: 1.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2022Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learningcited by 59
  3. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  4. 2022A Survey on Over-the-Air Computationcited by 19
  5. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  6. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  7. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
  8. 2021Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learningcited by 10
  9. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  10. 2021Relay-Assisted Cooperative Federated Learningcited by 9
  11. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 7
  12. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
20 more
  1. 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
  2. 2022Differentially Private Federated Learning via Reconfigurable Intelligent Surfacecited by 5
  3. 2023Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channelscited by 5
  4. 2022CFLIT: Coexisting Federated Learning and Information Transfercited by 4
  5. 2021CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surfacecited by 3
  6. 2021Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learningcited by 2
  7. 2025Optimal Transceiver Design in Over-the-Air Federated Distillationcited by 2
  8. 2022Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensingcited by 1
  9. 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learningcited by 1
  10. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  11. 2024Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environmentscited by 1
  12. 2025Straggler-Resilient Federated Learning over A Hybrid Conventional and Pinching Antenna Networkcited by 1
  13. 2021Over-the-Air Federated Multi-Task Learningcited by 0
  14. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  15. 2023Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradientcited by 0
  16. 2023Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Frameworkcited by 0
  17. 2024Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfacescited by 0
  18. 2024A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learningcited by 0
  19. 2025Pinching Antennas Meet AI in Next-Generation Wireless Networkscited by 0
  20. 2026When Agentic AI Meets Integrated Sensing and Communicationcited by 0