Linear regression over networks with communication guarantees
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 o...
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| Format: | Conference item |
| Language: | English |
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Journal of Machine Learning Research
2021
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| _version_ | 1797108015943385088 |
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| author | Gatsis, K |
| author_facet | Gatsis, K |
| author_sort | Gatsis, K |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T07:23:35Z |
| format | Conference item |
| id | oxford-uuid:eac0f4ec-e673-49a3-8f83-c4a185984c7b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:23:35Z |
| publishDate | 2021 |
| publisher | Journal of Machine Learning Research |
| record_format | dspace |
| spelling | oxford-uuid:eac0f4ec-e673-49a3-8f83-c4a185984c7b2022-10-27T15:12:03ZLinear regression over networks with communication guaranteesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:eac0f4ec-e673-49a3-8f83-c4a185984c7bEnglishSymplectic ElementsJournal of Machine Learning Research2021Gatsis, KA 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. |
| spellingShingle | Gatsis, K Linear regression over networks with communication guarantees |
| title | Linear regression over networks with communication guarantees |
| title_full | Linear regression over networks with communication guarantees |
| title_fullStr | Linear regression over networks with communication guarantees |
| title_full_unstemmed | Linear regression over networks with communication guarantees |
| title_short | Linear regression over networks with communication guarantees |
| title_sort | linear regression over networks with communication guarantees |
| work_keys_str_mv | AT gatsisk linearregressionovernetworkswithcommunicationguarantees |