# Private Wireless Federated Learning with Anonymous Over-the-Air Computation

arXiv: https://arxiv.org/abs/2011.08579 · HTML: https://mlsanity.com/paper/2011.08579/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation](https://mlsanity.com/paper/2103.01953/) (2021)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Semi-Decentralized Federated Learning with Collaborative Relaying](https://mlsanity.com/paper/2205.10998/) (2022)
- [Over-the-Air Ensemble Inference with Model Privacy](https://mlsanity.com/paper/2202.03129/) (2022)
- [Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying](https://mlsanity.com/paper/2202.11850/) (2022)
- [Differentially Private Federated Learning via Reconfigurable Intelligent Surface](https://mlsanity.com/paper/2203.17028/) (2022)
- [Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations](https://mlsanity.com/paper/2210.02235/) (2022)
- [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)
- [Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks](https://mlsanity.com/paper/2406.03766/) (2024)
- [Over-the-Air Federated Learning with Retransmissions (Extended Version)](https://mlsanity.com/paper/2111.10267/) (2021)
- [AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2306.06825/) (2023)
- [Differentially Private Wireless Federated Learning Using Orthogonal Sequences](https://mlsanity.com/paper/2306.08280/) (2023)
- [Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework](https://mlsanity.com/paper/2412.04408/) (2024)
- [When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence](https://mlsanity.com/paper/2603.19040/) (2026)

## Abstract (arXiv)

In conventional federated learning (FL), differential privacy (DP) guarantees can be obtained by injecting additional noise to local model updates before transmitting to the parameter server (PS). In the wireless FL scenario, we show that the privacy of the system can be boosted by exploiting over-the-air computation (OAC) and anonymizing the transmitting devices. In OAC, devices transmit their model updates simultaneously and in an uncoded fashion, resulting in a much more efficient use of the available spectrum. We further exploit OAC to provide anonymity for the transmitting devices. The proposed approach improves the performance of private wireless FL by reducing the amount of noise that must be injected.