Wireless for machine learning: a survey
This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. It gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses applic...
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston ; Delft
Now
2022
|
| Schriftenreihe: | Foundations and trends in signal processing
Volume 15, issue 4 |
| Zusammenfassung: | This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. It gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios Intro -- Introduction -- Related Work -- Notation and Organization -- Primer on Distributed Machine Learning -- Problem Formulation for Centralized Machine Learning -- Problem Formulation for Distributed Machine Learning -- Federated Learning -- Summary -- Analog Over-the-air Computation -- Primer -- Over-the-air Computation For Distributed Machine Learning -- Review of SISO Over-the-air Computation -- Review of MIMO Over-the-air Computation -- Digital Communications -- Primer -- Digital Communications for Distributed Machine Learning -- Review of Importance-aware Communications -- Review of Radio Resource Management for Federated Learning -- Open Problems -- Over-the-air Computation -- Digital Communications -- Problems Relevant to Analog and Digital Communications -- Applications -- Smart City -- Vehicular Communication -- Augmented and Virtual Reality -- Edge Caching -- Unmanned Aerial Vehicles -- Conclusions -- References |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 115 Seiten |
| ISBN: | 9781638280064 |
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| 245 | 1 | 0 | |a Wireless for machine learning |b a survey |c Henrik Hellström (KTH Royal Institute of Technology), José Mairton B. da Silva Jr. (KTH Royal Institute of Technology), Mohammad Mohammadi Amiri (Massachusetts Institute of Technology), Mingzhe Chen (Princeton University), Viktoria Fodor (KTH Royal Institute of Technology), H. Vincent Poor (Princeton University), Carlo Fischione (KTH Royal Institute of Technology) |
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| 520 | 3 | |a This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. It gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios | |
| 520 | 3 | |a Intro -- Introduction -- Related Work -- Notation and Organization -- Primer on Distributed Machine Learning -- Problem Formulation for Centralized Machine Learning -- Problem Formulation for Distributed Machine Learning -- Federated Learning -- Summary -- Analog Over-the-air Computation -- Primer -- Over-the-air Computation For Distributed Machine Learning -- Review of SISO Over-the-air Computation -- Review of MIMO Over-the-air Computation -- Digital Communications -- Primer -- Digital Communications for Distributed Machine Learning -- Review of Importance-aware Communications -- Review of Radio Resource Management for Federated Learning -- Open Problems -- Over-the-air Computation -- Digital Communications -- Problems Relevant to Analog and Digital Communications -- Applications -- Smart City -- Vehicular Communication -- Augmented and Virtual Reality -- Edge Caching -- Unmanned Aerial Vehicles -- Conclusions -- References | |
| 700 | 1 | |a Silva, José Mairton B. da |c Jr. |e Verfasser |0 (DE-588)1263517188 |4 aut | |
| 700 | 1 | |a Mohammadi Amiri, Mohammad |e Verfasser |0 (DE-588)1263517773 |4 aut | |
| 700 | 1 | |a Chen, Mingzhe |e Verfasser |4 aut | |
| 700 | 1 | |a Fodor, Viktoria |e Verfasser |4 aut | |
| 700 | 1 | |a Poor, H. Vincent |d 1951- |e Verfasser |0 (DE-588)1069964891 |4 aut | |
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| id | DE-604.BV048376147 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T20:17:18Z |
| indexdate | 2024-07-10T09:36:24Z |
| institution | BVB |
| isbn | 9781638280064 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033755088 |
| oclc_num | 1337120590 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 115 Seiten |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Now |
| record_format | marc |
| series | Foundations and trends in signal processing |
| series2 | Foundations and trends in signal processing |
| spelling | Hellström, Henrik Verfasser aut Wireless for machine learning a survey Henrik Hellström (KTH Royal Institute of Technology), José Mairton B. da Silva Jr. (KTH Royal Institute of Technology), Mohammad Mohammadi Amiri (Massachusetts Institute of Technology), Mingzhe Chen (Princeton University), Viktoria Fodor (KTH Royal Institute of Technology), H. Vincent Poor (Princeton University), Carlo Fischione (KTH Royal Institute of Technology) Boston ; Delft Now 2022 115 Seiten txt rdacontent n rdamedia nc rdacarrier Foundations and trends in signal processing Volume 15, issue 4 Description based on publisher supplied metadata and other sources This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. It gives a comprehensive introduction to these methods, reviews the most important works, highlights open problems, and discusses application scenarios Intro -- Introduction -- Related Work -- Notation and Organization -- Primer on Distributed Machine Learning -- Problem Formulation for Centralized Machine Learning -- Problem Formulation for Distributed Machine Learning -- Federated Learning -- Summary -- Analog Over-the-air Computation -- Primer -- Over-the-air Computation For Distributed Machine Learning -- Review of SISO Over-the-air Computation -- Review of MIMO Over-the-air Computation -- Digital Communications -- Primer -- Digital Communications for Distributed Machine Learning -- Review of Importance-aware Communications -- Review of Radio Resource Management for Federated Learning -- Open Problems -- Over-the-air Computation -- Digital Communications -- Problems Relevant to Analog and Digital Communications -- Applications -- Smart City -- Vehicular Communication -- Augmented and Virtual Reality -- Edge Caching -- Unmanned Aerial Vehicles -- Conclusions -- References Silva, José Mairton B. da Jr. Verfasser (DE-588)1263517188 aut Mohammadi Amiri, Mohammad Verfasser (DE-588)1263517773 aut Chen, Mingzhe Verfasser aut Fodor, Viktoria Verfasser aut Poor, H. Vincent 1951- Verfasser (DE-588)1069964891 aut Fischione, Carlo Verfasser (DE-588)1253969841 aut Erscheint auch als Online-Ausgabe 978-1-63828-007-1 Foundations and trends in signal processing Volume 15, issue 4 (DE-604)BV035213228 15,4 |
| spellingShingle | Hellström, Henrik Silva, José Mairton B. da Jr Mohammadi Amiri, Mohammad Chen, Mingzhe Fodor, Viktoria Poor, H. Vincent 1951- Fischione, Carlo Wireless for machine learning a survey Foundations and trends in signal processing |
| title | Wireless for machine learning a survey |
| title_auth | Wireless for machine learning a survey |
| title_exact_search | Wireless for machine learning a survey |
| title_exact_search_txtP | Wireless for machine learning a survey |
| title_full | Wireless for machine learning a survey Henrik Hellström (KTH Royal Institute of Technology), José Mairton B. da Silva Jr. (KTH Royal Institute of Technology), Mohammad Mohammadi Amiri (Massachusetts Institute of Technology), Mingzhe Chen (Princeton University), Viktoria Fodor (KTH Royal Institute of Technology), H. Vincent Poor (Princeton University), Carlo Fischione (KTH Royal Institute of Technology) |
| title_fullStr | Wireless for machine learning a survey Henrik Hellström (KTH Royal Institute of Technology), José Mairton B. da Silva Jr. (KTH Royal Institute of Technology), Mohammad Mohammadi Amiri (Massachusetts Institute of Technology), Mingzhe Chen (Princeton University), Viktoria Fodor (KTH Royal Institute of Technology), H. Vincent Poor (Princeton University), Carlo Fischione (KTH Royal Institute of Technology) |
| title_full_unstemmed | Wireless for machine learning a survey Henrik Hellström (KTH Royal Institute of Technology), José Mairton B. da Silva Jr. (KTH Royal Institute of Technology), Mohammad Mohammadi Amiri (Massachusetts Institute of Technology), Mingzhe Chen (Princeton University), Viktoria Fodor (KTH Royal Institute of Technology), H. Vincent Poor (Princeton University), Carlo Fischione (KTH Royal Institute of Technology) |
| title_short | Wireless for machine learning |
| title_sort | wireless for machine learning a survey |
| title_sub | a survey |
| volume_link | (DE-604)BV035213228 |
| work_keys_str_mv | AT hellstromhenrik wirelessformachinelearningasurvey AT silvajosemairtonbda wirelessformachinelearningasurvey AT mohammadiamirimohammad wirelessformachinelearningasurvey AT chenmingzhe wirelessformachinelearningasurvey AT fodorviktoria wirelessformachinelearningasurvey AT poorhvincent wirelessformachinelearningasurvey AT fischionecarlo wirelessformachinelearningasurvey |