# Federated Learning in Wireless Networks via Over-the-Air Computations

arXiv: https://arxiv.org/abs/2305.04630 · HTML: https://mlsanity.com/paper/2305.04630/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [The Convergence of Sparsified Gradient Methods](https://mlsanity.com/paper/1809.10505/) (2018)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [On the Convergence of Decentralized Gradient Descent](https://mlsanity.com/paper/1310.7063/) (2013)
- [Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients](https://mlsanity.com/paper/2102.07053/) (2021)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [Over-the-Air Federated Learning from Heterogeneous Data](https://mlsanity.com/paper/2009.12787/) (2020)
- [A general framework for decentralized optimization with first-order methods](https://mlsanity.com/paper/2009.05837/) (2020)
- [Linear convergence in optimization over directed graphs with row-stochastic matrices](https://mlsanity.com/paper/1611.06160/) (2016)
- [Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air](https://mlsanity.com/paper/1901.00844/) (2019)
- [Over-The-Air Computation in Correlated Channels](https://mlsanity.com/paper/2101.04690/) (2021)
- [Distributed Optimization over Directed Graphs with Row Stochasticity and Constraint Regularity](https://mlsanity.com/paper/1806.07081/) (2018)
- [Max-Consensus Over Fading Wireless Channels](https://mlsanity.com/paper/2005.05629/) (2020)
- [Distributed Convex Optimization with Inequality Constraints over Time-varying Unbalanced Digraphs](https://mlsanity.com/paper/1612.09029/) (2016)

## Built on by (most cited first, 2 of 2)

- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Boosting Fairness and Robustness in Over-the-Air Federated Learning](https://mlsanity.com/paper/2403.04431/) (2024)

## Abstract (arXiv)

In a multi-agent system, agents can cooperatively learn a model from data by exchanging their estimated model parameters, without the need to exchange the locally available data used by the agents. This strategy, often called federated learning, is mainly employed for two reasons: (i) improving resource-efficiency by avoiding to share potentially large datasets and (ii) guaranteeing privacy of local agents' data. Efficiency can be further increased by adopting a beyond-5G communication strategy that goes under the name of Over-the-Air Computation. This strategy exploits the interference property of the wireless channel. Standard communication schemes prevent interference by enabling transmissions of signals from different agents at distinct time or frequency slots, which is not required with Over-the-Air Computation, thus saving resources. In this case, the received signal is a weighted sum of transmitted signals, with unknown weights (fading channel coefficients). State of the art papers in the field aim at reconstructing those unknown coefficients. In contrast, the approach presented here does not require reconstructing channel coefficients by complex encoding-decoding schemes. This improves both efficiency and privacy.