Management of Resource at the Network Edge for Federated Learning Article Swipe
Silvana Trindade
,
Luiz F. Bittencourt
,
Nelson L. S. da Fonseca
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.03428
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.03428
Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we describe the recent work on resource management at the edge, and explore the challenges and future directions to allow the execution of federated learning at the edge. Some of the problems of this management, such as discovery of resources, deployment, load balancing, migration, and energy efficiency will be discussed in the paper.
Related Topics
Concepts
Software deployment
Computer science
Enhanced Data Rates for GSM Evolution
Edge device
Distributed computing
Edge computing
Shared resource
Work (physics)
Resource (disambiguation)
Data science
Knowledge management
Computer network
Software engineering
Artificial intelligence
Engineering
Operating system
Mechanical engineering
Cloud computing
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2107.03428
- https://arxiv.org/pdf/2107.03428
- OA Status
- green
- References
- 107
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3181699458
All OpenAlex metadata
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https://openalex.org/W3181699458Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2107.03428Digital Object Identifier
- Title
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Management of Resource at the Network Edge for Federated LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-07-07Full publication date if available
- Authors
-
Silvana Trindade, Luiz F. Bittencourt, Nelson L. S. da FonsecaList of authors in order
- Landing page
-
https://arxiv.org/abs/2107.03428Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2107.03428Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2107.03428Direct OA link when available
- Concepts
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Software deployment, Computer science, Enhanced Data Rates for GSM Evolution, Edge device, Distributed computing, Edge computing, Shared resource, Work (physics), Resource (disambiguation), Data science, Knowledge management, Computer network, Software engineering, Artificial intelligence, Engineering, Operating system, Mechanical engineering, Cloud computingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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107Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| referenced_works_count | 107 |
| abstract_inverted_index.a | 6 |
| abstract_inverted_index.In | 47 |
| abstract_inverted_index.as | 5, 86 |
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| abstract_inverted_index.be | 45, 98 |
| abstract_inverted_index.in | 100 |
| abstract_inverted_index.of | 30, 72, 79, 82, 88 |
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| abstract_inverted_index.to | 18, 68 |
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| abstract_inverted_index.and | 61, 65, 94 |
| abstract_inverted_index.are | 40 |
| abstract_inverted_index.end | 15 |
| abstract_inverted_index.for | 9 |
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| abstract_inverted_index.the | 12, 28, 36, 52, 59, 63, 70, 76, 80, 101 |
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| abstract_inverted_index.this | 48, 83 |
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| abstract_inverted_index.other | 25 |
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| abstract_inverted_index.where | 14, 38 |
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| abstract_inverted_index.future | 66 |
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| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |