# Wireless Federated Learning with Local Differential Privacy

arXiv: https://arxiv.org/abs/2002.05151 · HTML: https://mlsanity.com/paper/2002.05151/

## Key facts

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

## Built on by (most cited first, 30 of 52)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication](https://mlsanity.com/paper/2102.04737/) (2021)
- [No Free Lunch Theorem for Security and Utility in Federated Learning](https://mlsanity.com/paper/2203.05816/) (2022)
- [Joint Privacy Enhancement and Quantization in Federated Learning](https://mlsanity.com/paper/2208.10888/) (2022)
- [Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy](https://mlsanity.com/paper/2202.07178/) (2022)
- [Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses](https://mlsanity.com/paper/2106.09779/) (2021)
- [Private Non-Convex Federated Learning Without a Trusted Server](https://mlsanity.com/paper/2203.06735/) (2022)
- [Low-Latency Federated Learning over Wireless Channels with Differential Privacy](https://mlsanity.com/paper/2106.13039/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation](https://mlsanity.com/paper/2103.01953/) (2021)
- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Transparent Contribution Evaluation for Secure Federated Learning on Blockchain](https://mlsanity.com/paper/2101.10572/) (2021)
- [Accelerated Gradient Descent Learning over Multiple Access Fading Channels](https://mlsanity.com/paper/2107.12452/) (2021)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Over-the-Air Federated Learning via Second-Order Optimization](https://mlsanity.com/paper/2203.15488/) (2022)
- [Over-the-Air Ensemble Inference with Model Privacy](https://mlsanity.com/paper/2202.03129/) (2022)
- [Wireless Federated Learning with Limited Communication and Differential Privacy](https://mlsanity.com/paper/2106.00564/) (2021)
- [Locally Differentially Private Online Federated Learning With Correlated Noise](https://mlsanity.com/paper/2411.18752/) (2024)
- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations](https://mlsanity.com/paper/2210.02235/) (2022)
- [Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy](https://mlsanity.com/paper/2304.07460/) (2023)
- [Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels](https://mlsanity.com/paper/2306.10982/) (2023)
- [Private Collaborative Edge Inference via Over-the-Air Computation](https://mlsanity.com/paper/2407.21151/) (2024)
- [RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning](https://mlsanity.com/paper/2306.00675/) (2023)
- [Over-the-Air Statistical Estimation](https://mlsanity.com/paper/2103.04014/) (2021)
- [Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo](https://mlsanity.com/paper/2202.13932/) (2022)

## 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.