Linear Regression over Networks with Communication Guarantees
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 6 Mar 2021.
Builds on
18 of 24 references.
References · 18
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6 more
- 2019Age-of-Information vs. Value-of-Information Scheduling for Cellular Networked Control Systemscited by 11
- 2020Communication Efficient Federated Learning over Multiple Access Channelscited by 11
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- 2021Adaptive Scheduling for Machine Learning Tasks over Networkscited by 2
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Citing papers per year: 2021: 2.
2 papers.