mlsanityData to 7 Oct 2026

Federated Learning via Intelligent Reflecting Surface

arXiv 2011.05051 ↗First posted 10 Nov 2020cs.IT cs.LG eess.SPcited by 38

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Abstract (arXiv)

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we formulate an optimization problem that jointly optimizes the device selection, the aggregation beamformer at the base station (BS), and the phase shifts at the IRS to maximize the number of devices participating in the model aggregation of each communication round under certain mean-squared-error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex-functions programming algorithm for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results will demonstrate that our proposed algorithm and the deployment of an IRS can achieve a lower training loss and higher FL prediction accuracy than the baseline algorithms.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Nov 2020.

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Citing papers per year: 2021: 6; 2022: 12; 2023: 10; 2024: 6; 2025: 1; 2026: 3.

38 papers.

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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. 2021Over-the-Air Decentralized Federated Learningcited by 18
  5. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  6. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  7. 2022Trustworthy Federated Learning via Blockchaincited by 15
  8. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  9. 2024A Survey on Integrated Sensing, Communication, and Computationcited by 12
  10. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
  11. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  12. 2023Over-the-Air Federated Learning and Optimizationcited by 10
26 more
  1. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  2. 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interferencecited by 9
  3. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  4. 2022Differentially Private Federated Learning via Reconfigurable Intelligent Surfacecited by 5
  5. 2022Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbationscited by 5
  6. 2024Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Aircited by 5
  7. 2024Over-the-Air Federated Learning via Weighted Aggregationcited by 5
  8. 2022Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Controlcited by 4
  9. 2023Integrated Sensing-Communication-Computation for Edge Artificial Intelligencecited by 4
  10. 2021CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surfacecited by 3
  11. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  12. 2021Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learningcited by 2
  13. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  14. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  15. 2023Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networkscited by 2
  16. 2021Over-the-Air Federated Learning with Retransmissions (Extended Version)cited by 1
  17. 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learningcited by 1
  18. 2023Digital Over-the-Air Federated Learning in Multi-Antenna Systemscited by 1
  19. 2023Towards Scalable Wireless Federated Learning: Challenges and Solutionscited by 1
  20. 2024Federated Learning for distribution skewed data using sample weightscited by 1
  21. 2026Energy-Efficient Over-the-Air Federated Learning via Pinching Antenna Systemscited by 1
  22. 2022Need of 6G for the Metaverse Realizationcited by 0
  23. 2023Joint Power Control and Data Size Selection for Over-the-Air Computation Aided Federated Learningcited by 0
  24. 2024Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfacescited by 0
  25. 2026Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networkscited by 0
  26. 2026AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systemscited by 0