# Communication Efficient Federated Learning over Multiple Access Channels

arXiv: https://arxiv.org/abs/2001.08737 · HTML: https://mlsanity.com/paper/2001.08737/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [Wireless Quantized Federated Learning: A Joint Computation and Communication Design](https://mlsanity.com/paper/2203.05878/) (2022)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [Accelerated Gradient Descent Learning over Multiple Access Fading Channels](https://mlsanity.com/paper/2107.12452/) (2021)
- [Over-the-Air Federated Learning via Second-Order Optimization](https://mlsanity.com/paper/2203.15488/) (2022)
- [Federated Learning with Downlink Device Selection](https://mlsanity.com/paper/2107.03510/) (2021)
- [Fundamental limits of over-the-air optimization: Are analog schemes optimal?](https://mlsanity.com/paper/2109.05222/) (2021)
- [Adaptive Scheduling for Machine Learning Tasks over Networks](https://mlsanity.com/paper/2101.10007/) (2021)
- [Linear Regression over Networks with Communication Guarantees](https://mlsanity.com/paper/2103.04140/) (2021)
- [Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO](https://mlsanity.com/paper/2212.06482/) (2022)
- [Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth](https://mlsanity.com/paper/2405.03248/) (2024)
- [Timely Asynchronous Hierarchical Federated Learning: Age of Convergence](https://mlsanity.com/paper/2306.12400/) (2023)

## Abstract (arXiv)

In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due to FL's iterative nature and large model sizes. One recent direction to alleviate communication bottleneck in FL is to let users communicate simultaneously over a multiple access channel (MAC), possibly making better use of the communication resources. In this paper, we consider the problem of FL learning over a MAC. In particular, we focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS. When designing digital FL schemes over MACs, there are new opportunities to assign different amount of resources (such as rate or bandwidth) to different users based on a) the informativeness of the gradients at each user, and b) the underlying channel conditions. We propose a stochastic gradient quantization scheme, where the quantization parameters are optimized based on the capacity region of the MAC. We show that such channel aware quantization for FL outperforms uniform quantization, particularly when users experience different channel conditions, and when have gradients with varying levels of informativeness.