# Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework

arXiv: https://arxiv.org/abs/2412.04408 · HTML: https://mlsanity.com/paper/2412.04408/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control](https://mlsanity.com/paper/2006.05459/) (2020)
- [Wireless Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2002.05151/) (2020)
- [Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation](https://mlsanity.com/paper/2103.01953/) (2021)
- [Private Wireless Federated Learning with Anonymous Over-the-Air Computation](https://mlsanity.com/paper/2011.08579/) (2020)
- [Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise](https://mlsanity.com/paper/2004.06337/) (2020)
- [Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control](https://mlsanity.com/paper/2205.05867/) (2022)
- [Wireless Federated Learning with Limited Communication and Differential Privacy](https://mlsanity.com/paper/2106.00564/) (2021)
- [CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks](https://mlsanity.com/paper/2205.09330/) (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)
- [Privacy-Enhanced Over-the-Air Federated Learning via Client-Driven Power Balancing](https://mlsanity.com/paper/2410.05907/) (2024)
- [ROAR-Fed: RIS-Assisted Over-the-Air Adaptive Resource Allocation for Federated Learning](https://mlsanity.com/paper/2309.09883/) (2023)
- [RIS-Assisted Over-the-Air Adaptive Federated Learning with Noisy Downlink](https://mlsanity.com/paper/2309.10758/) (2023)
- [Over-the-Air Federated Averaging with Limited Power and Privacy Budgets](https://mlsanity.com/paper/2305.03547/) (2023)
- [Differentially Private Wireless Federated Learning Using Orthogonal Sequences](https://mlsanity.com/paper/2306.08280/) (2023)
- [Federated Learning with Reduced Information Leakage and Computation](https://mlsanity.com/paper/2310.06341/) (2023)

## Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning framework that inherently allows edge devices to maintain their local training data, thus providing some level of privacy. However, FL's model updates still pose a risk of privacy leakage, which must be mitigated. Over-the-air FL (OTA-FL) is an adapted FL design for wireless edge networks that leverages the natural superposition property of the wireless medium. We propose a wireless physical layer (PHY) design for OTA-FL which improves differential privacy (DP) through a decentralized, dynamic power control that utilizes both inherent Gaussian noise in the wireless channel and a cooperative jammer (CJ) for additional artificial noise generation when higher privacy levels are required. Although primarily implemented within the Upcycled-FL framework, where a resource-efficient method with first-order approximations is used at every even iteration to decrease the required information from clients, our power control strategy is applicable to any FL framework, including FedAvg and FedProx as shown in the paper. This adaptation showcases the flexibility and effectiveness of our design across different learning algorithms while maintaining a strong emphasis on privacy. Our design removes the need for client-side artificial noise injection for DP, utilizing a cooperative jammer to enhance privacy without affecting transmission efficiency for higher privacy demands. Privacy analysis is provided using the Moments Accountant method. We perform a convergence analysis for non-convex objectives to tackle heterogeneous data distributions, highlighting the inherent trade-offs between privacy and accuracy. Numerical results show that our approach with various FL algorithms outperforms the state-of-the-art under the same DP conditions on the non-i.i.d. FEMNIST dataset, and highlight the cooperative jammer's effectiveness in ensuring strict privacy.