# Decentralized Wireless Federated Learning with Differential Privacy

arXiv: https://arxiv.org/abs/2109.09142 · HTML: https://mlsanity.com/paper/2109.09142/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2021.

## Built on by (most cited first, 18 of 18)

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition](https://mlsanity.com/paper/2304.07421/) (2023)
- [Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey](https://mlsanity.com/paper/2312.08667/) (2023)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [Exactly Minimax-Optimal Locally Differentially Private Sampling](https://mlsanity.com/paper/2410.22699/) (2024)
- [On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations](https://mlsanity.com/paper/2208.11848/) (2022)
- [Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning](https://mlsanity.com/paper/2407.04460/) (2024)
- [Performance Analysis of Decentralized Federated Learning Deployments](https://mlsanity.com/paper/2503.11828/) (2025)
- [FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification](https://mlsanity.com/paper/2503.18981/) (2025)
- [Onboard Optimization and Learning: A Survey](https://mlsanity.com/paper/2505.08793/) (2025)
- [A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques](https://mlsanity.com/paper/2403.11163/) (2024)
- [Convergent Differential Privacy Analysis for General Federated Learning](https://mlsanity.com/paper/2408.15621/) (2024)
- [Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning](https://mlsanity.com/paper/2501.04409/) (2025)
- [Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?](https://mlsanity.com/paper/2506.12749/) (2025)
- [DFCA: Decentralized Federated Clustering Algorithm](https://mlsanity.com/paper/2510.15300/) (2025)
- [DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data](https://mlsanity.com/paper/2606.04399/) (2026)
- [Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning](https://mlsanity.com/paper/2606.22878/) (2026)

## Abstract (arXiv)

This paper studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a Decentralized-Wireless-Federated-Learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies (ε,δ)-differential privacy and the privacy budget per worker scales as O(frac1√N), in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of O(√frac1TN) as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.