# Linear Regression over Networks with Communication Guarantees

arXiv: https://arxiv.org/abs/2103.04140 · HTML: https://mlsanity.com/paper/2103.04140/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 6 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [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)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Sparse Communication for Distributed Gradient Descent](https://mlsanity.com/paper/1704.05021/) (2017)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning](https://mlsanity.com/paper/1805.09965/) (2018)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication](https://mlsanity.com/paper/1805.08768/) (2018)
- [Machine Learning in the Air](https://mlsanity.com/paper/1904.12385/) (2019)
- [Cooperative Learning via Federated Distillation over Fading Channels](https://mlsanity.com/paper/2002.01337/) (2020)
- [Age-of-Information vs. Value-of-Information Scheduling for Cellular Networked Control Systems](https://mlsanity.com/paper/1903.05356/) (2019)
- [Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning](https://mlsanity.com/paper/1812.03239/) (2018)
- [Communication Efficient Federated Learning over Multiple Access Channels](https://mlsanity.com/paper/2001.08737/) (2020)
- [Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems](https://mlsanity.com/paper/1811.00409/) (2018)
- [Event-triggered Learning for Resource-efficient Networked Control](https://mlsanity.com/paper/1803.01802/) (2018)
- [Event-Based Control for Online Training of Neural Networks](https://mlsanity.com/paper/2003.09503/) (2020)
- [Adaptive Scheduling for Machine Learning Tasks over Networks](https://mlsanity.com/paper/2101.10007/) (2021)

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

- [Federated Reinforcement Learning at the Edge](https://mlsanity.com/paper/2112.05908/) (2021)
- [Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime](https://mlsanity.com/paper/2112.05837/) (2021)

## Abstract (arXiv)

A key functionality of emerging connected autonomous systems such as smart cities, smart transportation systems, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations. This is increasingly attracting attention under the terms of distributed learning and federated learning. However, in connected autonomous systems, data transfer takes place over communication networks with often limited resources. This paper examines algorithms for communication-efficient learning for linear regression tasks by exploiting the informativeness of the data. The developed algorithms enable a tradeoff between communication and learning with theoretical performance guarantees and efficient practical implementations.