# Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning

arXiv: https://arxiv.org/abs/2007.01790 · HTML: https://mlsanity.com/paper/2007.01790/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 24 of 24)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Over-the-Air Federated Learning via Second-Order Optimization](https://mlsanity.com/paper/2203.15488/) (2022)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Temporal-Structure-Assisted Gradient Aggregation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2103.02270/) (2021)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation](https://mlsanity.com/paper/2106.00999/) (2021)
- [AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning](https://mlsanity.com/paper/2105.00395/) (2021)
- [Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy](https://mlsanity.com/paper/2304.07460/) (2023)
- [Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning](https://mlsanity.com/paper/2306.10299/) (2023)
- [Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels](https://mlsanity.com/paper/2106.00995/) (2021)
- [Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs](https://mlsanity.com/paper/2110.01686/) (2021)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO](https://mlsanity.com/paper/2212.06482/) (2022)
- [Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent](https://mlsanity.com/paper/2105.14772/) (2021)
- [A New Implementation of Federated Learning for Privacy and Security Enhancement](https://mlsanity.com/paper/2208.01826/) (2022)
- [Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning](https://mlsanity.com/paper/2312.14638/) (2023)
- [GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning](https://mlsanity.com/paper/2412.03867/) (2024)
- [Rethinking Federated Learning Over the Air: The Blessing of Scaling Up](https://mlsanity.com/paper/2508.17697/) (2025)
- [Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks](https://mlsanity.com/paper/2108.02517/) (2021)
- [Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning](https://mlsanity.com/paper/2506.14251/) (2025)
- [Timely Parameter Updating in Over-the-Air Federated Learning](https://mlsanity.com/paper/2512.19103/) (2025)
- [Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data](https://mlsanity.com/paper/2609.14246/) (2026)

## Abstract (arXiv)

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth. To address these challenges, in this work we formulate a novel constrained optimization problem, and propose an FL framework harnessing wireless channel perturbations and interference for improving privacy, bandwidth-efficiency, and scalability. The resultant algorithm is coined analog federated ADMM (A-FADMM) based on analog transmissions and the alternating direction method of multipliers (ADMM). In A-FADMM, all workers upload their model updates to the parameter server (PS) using a single channel via analog transmissions, during which all models are perturbed and aggregated over-the-air. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper including the honest-but-curious PS, thereby preserving data privacy against model inversion attacks. We formally prove the convergence and privacy guarantees of A-FADMM for convex functions under time-varying channels, and numerically show the effectiveness of A-FADMM under noisy channels and stochastic non-convex functions, in terms of convergence speed and scalability, as well as communication bandwidth and energy efficiency.