Stochastic Optimization From Distributed Streaming Data in Rate-Limited Networks Article Swipe
Matthew Nokleby
,
Waheed U. Bajwa
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/tsipn.2018.2866320
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/tsipn.2018.2866320
Motivated by machine learning applications in networks of sensors,\ninternet-of-things (IoT) devices, and autonomous agents, we propose techniques\nfor distributed stochastic convex learning from high-rate data streams. The\nsetup involves a network of nodes---each one of which has a stream of data\narriving at a constant rate---that solve a stochastic convex optimization\nproblem by collaborating with each other over rate-limited communication links.\nTo this end, we present and analyze two algorithms---termed distributed\nstochastic approximation mirror descent (D-SAMD) and accelerated distributed\nstochastic approximation mirror descent (AD-SAMD)---that are based on two\nstochastic variants of mirror descent and in which nodes collaborate via\napproximate averaging of the local, noisy subgradients using distributed\nconsensus. Our main contributions are (i) bounds on the convergence rates of\nD-SAMD and AD-SAMD in terms of the number of nodes, network topology, and ratio\nof the data streaming and communication rates, and (ii) sufficient conditions\nfor order-optimum convergence of these algorithms. In particular, we show that\nfor sufficiently well-connected networks, distributed learning schemes can\nobtain order-optimum convergence even if the communications rate is small.\nFurther we find that the use of accelerated methods significantly enlarges the\nregime in which order-optimum convergence is achieved; this is in contrast to\nthe centralized setting, where accelerated methods usually offer only a modest\nimprovement. Finally, we demonstrate the effectiveness of the proposed\nalgorithms using numerical experiments.\n
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tsipn.2018.2866320
- OA Status
- green
- Cited By
- 11
- References
- 42
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2611809118Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tsipn.2018.2866320Digital Object Identifier
- Title
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Stochastic Optimization From Distributed Streaming Data in Rate-Limited NetworksWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-08-20Full publication date if available
- Authors
-
Matthew Nokleby, Waheed U. BajwaList of authors in order
- Landing page
-
https://doi.org/10.1109/tsipn.2018.2866320Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1704.07888Direct OA link when available
- Concepts
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Computer science, Streaming data, Stochastic optimization, Distributed computing, Mathematical optimization, Mathematics, Data miningTop concepts (fields/topics) attached by OpenAlex
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-
11Total citation count in OpenAlex
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2024: 1, 2023: 1, 2021: 3, 2020: 4, 2019: 1Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.offer | 187 |
| abstract_inverted_index.other | 52 |
| abstract_inverted_index.rates | 108 |
| abstract_inverted_index.solve | 43 |
| abstract_inverted_index.terms | 113 |
| abstract_inverted_index.these | 136 |
| abstract_inverted_index.using | 97, 199 |
| abstract_inverted_index.where | 183 |
| abstract_inverted_index.which | 33, 87, 171 |
| abstract_inverted_index.bounds | 104 |
| abstract_inverted_index.convex | 19, 46 |
| abstract_inverted_index.local, | 94 |
| abstract_inverted_index.mirror | 67, 74, 83 |
| abstract_inverted_index.nodes, | 118 |
| abstract_inverted_index.number | 116 |
| abstract_inverted_index.rates, | 128 |
| abstract_inverted_index.stream | 36 |
| abstract_inverted_index.AD-SAMD | 111 |
| abstract_inverted_index.agents, | 13 |
| abstract_inverted_index.analyze | 62 |
| abstract_inverted_index.descent | 68, 75, 84 |
| abstract_inverted_index.machine | 2 |
| abstract_inverted_index.methods | 166, 185 |
| abstract_inverted_index.network | 28, 119 |
| abstract_inverted_index.present | 60 |
| abstract_inverted_index.propose | 15 |
| abstract_inverted_index.schemes | 148 |
| abstract_inverted_index.to\nthe | 180 |
| abstract_inverted_index.usually | 186 |
| abstract_inverted_index.(D-SAMD) | 69 |
| abstract_inverted_index.Finally, | 191 |
| abstract_inverted_index.constant | 41 |
| abstract_inverted_index.contrast | 179 |
| abstract_inverted_index.devices, | 10 |
| abstract_inverted_index.enlarges | 168 |
| abstract_inverted_index.involves | 26 |
| abstract_inverted_index.learning | 3, 20, 147 |
| abstract_inverted_index.networks | 6 |
| abstract_inverted_index.setting, | 182 |
| abstract_inverted_index.streams. | 24 |
| abstract_inverted_index.variants | 81 |
| abstract_inverted_index.Motivated | 0 |
| abstract_inverted_index.achieved; | 175 |
| abstract_inverted_index.averaging | 91 |
| abstract_inverted_index.high-rate | 22 |
| abstract_inverted_index.networks, | 145 |
| abstract_inverted_index.numerical | 200 |
| abstract_inverted_index.ratio\nof | 122 |
| abstract_inverted_index.streaming | 125 |
| abstract_inverted_index.that\nfor | 142 |
| abstract_inverted_index.topology, | 120 |
| abstract_inverted_index.The\nsetup | 25 |
| abstract_inverted_index.autonomous | 12 |
| abstract_inverted_index.links.\nTo | 56 |
| abstract_inverted_index.of\nD-SAMD | 109 |
| abstract_inverted_index.stochastic | 18, 45 |
| abstract_inverted_index.sufficient | 131 |
| abstract_inverted_index.accelerated | 71, 165, 184 |
| abstract_inverted_index.algorithms. | 137 |
| abstract_inverted_index.can\nobtain | 149 |
| abstract_inverted_index.centralized | 181 |
| abstract_inverted_index.collaborate | 89 |
| abstract_inverted_index.convergence | 107, 134, 151, 173 |
| abstract_inverted_index.demonstrate | 193 |
| abstract_inverted_index.distributed | 17, 146 |
| abstract_inverted_index.particular, | 139 |
| abstract_inverted_index.rate---that | 42 |
| abstract_inverted_index.the\nregime | 169 |
| abstract_inverted_index.applications | 4 |
| abstract_inverted_index.nodes---each | 30 |
| abstract_inverted_index.rate-limited | 54 |
| abstract_inverted_index.subgradients | 96 |
| abstract_inverted_index.sufficiently | 143 |
| abstract_inverted_index.approximation | 66, 73 |
| abstract_inverted_index.collaborating | 49 |
| abstract_inverted_index.communication | 55, 127 |
| abstract_inverted_index.contributions | 101 |
| abstract_inverted_index.effectiveness | 195 |
| abstract_inverted_index.order-optimum | 133, 150, 172 |
| abstract_inverted_index.significantly | 167 |
| abstract_inverted_index.communications | 155 |
| abstract_inverted_index.data\narriving | 38 |
| abstract_inverted_index.experiments.\n | 201 |
| abstract_inverted_index.well-connected | 144 |
| abstract_inverted_index.conditions\nfor | 132 |
| abstract_inverted_index.small.\nFurther | 158 |
| abstract_inverted_index.techniques\nfor | 16 |
| abstract_inverted_index.two\nstochastic | 80 |
| abstract_inverted_index.(AD-SAMD)---that | 76 |
| abstract_inverted_index.via\napproximate | 90 |
| abstract_inverted_index.algorithms---termed | 64 |
| abstract_inverted_index.modest\nimprovement. | 190 |
| abstract_inverted_index.proposed\nalgorithms | 198 |
| abstract_inverted_index.optimization\nproblem | 47 |
| abstract_inverted_index.distributed\nconsensus. | 98 |
| abstract_inverted_index.distributed\nstochastic | 65, 72 |
| abstract_inverted_index.sensors,\ninternet-of-things | 8 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.85330141 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |