mlsanityData to 14 Oct 2026

Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning

arXiv 2207.08057 ↗First posted 17 Jul 2022cs.LG cs.IT eess.SPcited by 8

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Abstract (arXiv)

Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of AirFL is vulnerable due to the obscurity of the local models aggregated over-the-air. This paper presents a novel framework to balance the accuracy and integrity of AirFL, where multi-antenna devices and base station (BS) are jointly optimized with a reconfigurable intelligent surface (RIS). The key contributions include a new and non-trivial problem jointly considering the model accuracy and integrity of AirFL, and a new framework that transforms the problem into tractable subproblems. Under perfect channel state information (CSI), the new framework minimizes the aggregated model's distortion and retains the local models' recoverability by optimizing the transmit beamformers of the devices, the receive beamformers of the BS, and the RIS configuration in an alternating manner. Under imperfect CSI, the new framework delivers a robust design of the beamformers and RIS configuration to combat non-negligible channel estimation errors. As corroborated experimentally, the novel framework can achieve comparable accuracy to the ideal FL while preserving local model recoverability under perfect CSI, and improve the accuracy when the number of receive antennas is small or moderate under imperfect CSI.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 25 of its 58 references that are in the map. First posted on arXiv 17 Jul 2022.

Builds on

25 of 58 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2018Federated Learning via Over-the-Air Computationcited by 219
  5. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  6. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  7. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  8. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  9. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  10. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  11. 2018Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamformingcited by 46
  12. 2020Blind Federated Edge Learningcited by 43
13 more
  1. 2020Reconfigurable Intelligent Surfaces: Principles and Opportunitiescited by 35
  2. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 29
  3. 2019Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networkscited by 18
  4. 2021Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?cited by 14
  5. 2018Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networkscited by 11
  6. 2020Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approachcited by 8
  7. 2021STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimizationcited by 8
  8. 2020Federated Learning in Multi-RIS Aided Systemscited by 7
  9. 2020Resource Allocation for Multi-Cell IRS-Aided NOMA Networkscited by 4
  10. 2020Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSIcited by 3
  11. 2021Anchor-Assisted Channel Estimation for Intelligent Reflecting Surface Aided Multiuser Communicationcited by 2
  12. 2021QoS-Constrained Federated Learning Empowered by Intelligent Reflecting Surfacecited by 2
  13. 2016Modeling and Analysis of Uplink Non-Orthogonal Multiple Access (NOMA) in Large-Scale Cellular Networks Using Poisson Cluster Processescited by 1

Built on by

Cited by year

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

8 papers.

  1. 2022A Survey on Over-the-Air Computationcited by 21
  2. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 18
  3. 2023Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learningcited by 11
  4. 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
  5. 2023Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocationcited by 5
  6. 2023Blockchained Federated Learning for Internet of Things: A Comprehensive Surveycited by 5
  7. 2024Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregationcited by 1
  8. 2023Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Frameworkcited by 0