mlsanityData to 14 Oct 2026

Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions

arXiv 2307.00974 ↗First posted 3 Jul 2023eess.SP cs.DC cs.LGcited by 21

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Abstract (arXiv)

The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future. The resulting demand for the aggregation of large amounts of data has caused serious communication bottlenecks in wireless networks and particularly at the network edge. Over-the-air federated learning (OTA-FL), leveraging the superposition feature of multi-access channels (MACs), enables users at the network edge to share spectrum resources and achieves efficient and low-latency global model aggregation. This paper provides a holistic review of progress in OTA-FL and points to potential future research directions. Specifically, we classify OTA-FL from the perspective of system settings, including single-antenna OTA-FL, multi-antenna OTA-FL, and OTA-FL with the aid of the emerging reconfigurable intelligent surface (RIS) technology, and the contributions of existing works in these areas are summarized. Moreover, we discuss the trust, security and privacy aspects of OTA-FL, and highlight concerns arising from security and privacy. Finally, challenges and potential research directions are discussed to promote the future development of OTA-FL in terms of improving system performance, reliability, and trustworthiness. Specifical challenges to be addressed include model distortion under channel fading, the ineffective OTA aggregation of local models trained on substantially unbalanced data, and the limited accessibility and verifiability of individual local models.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 67 of its 126 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

67 of 126 references.

References · 67

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  6. 2018How To Backdoor Federated Learningcited by 765
  7. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  9. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  10. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  11. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  12. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
38 more
  1. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  2. 2018Federated Learning via Over-the-Air Computationcited by 219
  3. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  4. 2018Mitigating Sybils in Federated Learning Poisoningcited by 161
  5. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
  6. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  7. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
  8. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  9. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 63
  10. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
  11. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  12. 2020Wireless Federated Learning with Local Differential Privacycited by 52
  13. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 48
  14. 2021Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learningcited by 47
  15. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  16. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
  17. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 33
  18. 2020Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learningcited by 24
  19. 2020Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applicationscited by 21
  20. 2022Joint Device Selection and Power Control for Wireless Federated Learningcited by 20
  21. 2022Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systemscited by 20
  22. 2020Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learningcited by 19
  23. 2022When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worldscited by 19
  24. 2022Shielding Federated Learning: Robust Aggregation with Adaptive Client Selectioncited by 18
  25. 2020Private Wireless Federated Learning with Anonymous Over-the-Air Computationcited by 16
  26. 2020Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noisecited by 15
  27. 2022Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networkscited by 15
  28. 20206G White Paper on Edge Intelligencecited by 14
  29. 2021Joint Optimization of Communications and Federated Learning Over the Aircited by 14
  30. 2023Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learningcited by 14
  31. 2020Hybrid Beamforming for Massive MIMO Over-the-Air Computationcited by 13
  32. 2021Over-the-Air Computation via Reconfigurable Intelligent Surfacecited by 13
  33. 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
  34. 2018Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networkscited by 11
  35. 2022Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Designcited by 11
  36. 2022Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learningcited by 10
  37. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 8
  38. 2021Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selectioncited by 7

50 most cited of 67 All 67 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 8; 2025: 5; 2026: 5.

21 papers.

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  1. 2024Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunitiescited by 26
  2. 2023Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Surveycited by 18
  3. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  4. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  5. 2025Over-the-Air Edge Inference via End-to-End Metasurfaces-Integrated Artificial Neural Networkscited by 7
  6. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  7. 2024NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detectioncited by 4
  8. 2024Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clippingcited by 3
  9. 2024TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservationcited by 2
  10. 2024FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networkscited by 1
  11. 2024Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systemscited by 1
  12. 2025Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networkscited by 1
9 more
  1. 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
  2. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
  3. 2025Federated learning over physical channels: adaptive algorithms with near-optimal guaranteescited by 0
  4. 2025Timely Parameter Updating in Over-the-Air Federated Learningcited by 0
  5. 2026SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillationcited by 0
  6. 2026Event-Triggered Gossip for Distributed Learningcited by 0
  7. 2026Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learningcited by 0
  8. 2026FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learningcited by 0
  9. 2026FedFit: Federated Fine-Tuning of LLMs via Vector-Bank Parameterization and Quantizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control
  2. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  3. 2024Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework
  4. 2025Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off
  5. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
  6. 2024Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation
  7. 2022Over-the-Air Federated Learning via Second-Order Optimization
  8. 2026Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks