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Wireless Federated Learning with Local Differential Privacy

arXiv 2002.05151 ↗First posted 12 Feb 2020cs.CR cs.IT math.ITcited by 52

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Abstract (arXiv)

In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (LDP) constraints. We show that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong LDP guarantees for the users. We propose a private wireless gradient aggregation scheme, which shows that when aggregating gradients from K users, the privacy leakage per user scales as Obig(frac1√K big) compared to orthogonal transmission in which the privacy leakage scales as a constant. We also present analysis for the convergence rate of the proposed private FL aggregation algorithm and study the tradeoffs between wireless resources, convergence, and privacy.

Key facts. Cited by 52 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Feb 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 17; 2022: 11; 2023: 11; 2024: 8; 2025: 3; 2026: 2.

52 papers.

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  1. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  3. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  4. 2021Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communicationcited by 39
  5. 2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
  6. 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 35
  7. 2022Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacycited by 33
  8. 2021Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescited by 30
  9. 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
  10. 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 25
  11. 2023A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacycited by 24
  12. 2021Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregationcited by 22
38 more
  1. 2022A Survey on Over-the-Air Computationcited by 21
  2. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  3. 2021Transparent Contribution Evaluation for Secure Federated Learning on Blockchaincited by 16
  4. 2021Accelerated Gradient Descent Learning over Multiple Access Fading Channelscited by 14
  5. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  6. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
  7. 2022Over-the-Air Ensemble Inference with Model Privacycited by 10
  8. 2021Wireless Federated Learning with Limited Communication and Differential Privacycited by 6
  9. 2024Locally Differentially Private Online Federated Learning With Correlated Noisecited by 6
  10. 2022Differentially Private Federated Learning via Reconfigurable Intelligent Surfacecited by 5
  11. 2022Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networkscited by 5
  12. 2022Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbationscited by 5
  13. 2023Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacycited by 5
  14. 2023Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channelscited by 5
  15. 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 5
  16. 2023RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learningcited by 4
  17. 2021Over-the-Air Statistical Estimationcited by 3
  18. 2022Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlocited by 3
  19. 2023Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Surveycited by 3
  20. 2021On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Timescited by 2
  21. 2021DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identitiescited by 2
  22. 2021Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learningcited by 2
  23. 2024Age Aware Scheduling for Differentially-Private Federated Learningcited by 2
  24. 2025FedMPDD: Communication-Efficient Federated Learning with Privacy Preservation Attributes via Projected Directional Derivativecited by 2
  25. 2021Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacycited by 1
  26. 2021Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learningcited by 1
  27. 2023Over-the-Air Federated Averaging with Limited Power and Privacy Budgetscited by 1
  28. 2023Differentially Private Wireless Federated Learning Using Orthogonal Sequencescited by 1
  29. 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networkscited by 1
  30. 2023Federated Learning with Reduced Information Leakage and Computationcited by 1
  31. 2023A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learningcited by 1
  32. 2024Collaborative Inference over Wireless Channels with Feature Differential Privacycited by 1
  33. 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
  34. 2021Understanding the Interplay between Privacy and Robustness in Federated Learningcited by 0
  35. 2024Differentially Private Online Federated Learning with Correlated Noisecited by 0
  36. 2024Privacy-Preserving Federated Learning via Dataset Distillationcited by 0
  37. 2024Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Frameworkcited by 0
  38. 2025FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphscited by 0

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