Dynamic Network-Assisted D2D-Aided Coded Distributed Learning Article Swipe
Nikita Zeulin
,
Olga Galinina
,
Nageen Himayat
,
Sergey Andreev
,
Robert W. Heath
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.14789
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2111.14789
Today, various machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of a wireless network. Distributed real-time ML solutions are highly sensitive to the so-called straggler effect caused by resource heterogeneity and alleviated by various computation offloading mechanisms that seriously challenge the communication efficiency, especially in large-scale scenarios. To decrease the communication overhead, we rely on device-to-device (D2D) connectivity that improves spectrum utilization and allows efficient data exchange between devices in proximity. In particular, we design a novel D2D-aided coded federated learning method (D2D-CFL) for efficient load balancing across devices. The proposed solution captures system dynamics, including data (time-dependent learning model, varied intensity of data arrivals), device (diverse computational resources and volume of training data), and deployment (varied locations and D2D graph connectivity). To minimize the number of communication rounds, we derive an optimal compression rate for achieving minimum processing time and establish its connection with the convergence time. The resulting optimization problem provides suboptimal compression parameters, which improve the total training time. Our proposed method is beneficial for real-time collaborative applications, where the users continuously generate training data resulting in the model drift.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2111.14789
- https://arxiv.org/pdf/2111.14789
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226375740
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226375740Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2111.14789Digital Object Identifier
- Title
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Dynamic Network-Assisted D2D-Aided Coded Distributed LearningWork title
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-11-26Full publication date if available
- Authors
-
Nikita Zeulin, Olga Galinina, Nageen Himayat, Sergey Andreev, Robert W. HeathList of authors in order
- Landing page
-
https://arxiv.org/abs/2111.14789Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2111.14789Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2111.14789Direct OA link when available
- Concepts
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Computer science, Distributed computing, Overhead (engineering), Edge device, Enhanced Data Rates for GSM Evolution, Software deployment, Computation, Real-time computing, Artificial intelligence, Algorithm, Cloud computing, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.computation | 41 |
| abstract_inverted_index.convergence | 153 |
| abstract_inverted_index.efficiency, | 49 |
| abstract_inverted_index.large-scale | 52 |
| abstract_inverted_index.parameters, | 162 |
| abstract_inverted_index.particular, | 79 |
| abstract_inverted_index.utilization | 68 |
| abstract_inverted_index.applications | 5 |
| abstract_inverted_index.connectivity | 64 |
| abstract_inverted_index.continuously | 181 |
| abstract_inverted_index.optimization | 157 |
| abstract_inverted_index.applications, | 177 |
| abstract_inverted_index.collaborative | 176 |
| abstract_inverted_index.communication | 48, 57, 134 |
| abstract_inverted_index.computational | 114 |
| abstract_inverted_index.heterogeneity | 36 |
| abstract_inverted_index.connectivity). | 128 |
| abstract_inverted_index.(time-dependent | 104 |
| abstract_inverted_index.device-to-device | 62 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |