FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots Article Swipe
Ahmed Imteaj
,
M. Hadi Amini
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/icmla51294.2020.00185
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/icmla51294.2020.00185
Smartphones, autonomous vehicles, and the Internet-of-things (IoT) devices are considered the primary data source for a distributed network. Due to a revolutionary breakthrough in internet availability and continuous improvement of the IoT devices capabilities, it is desirable to store data locally and perform computation at the edge, as opposed to share all local information with a centralized computation agent. A recently proposed Machine Learning (ML) algorithm called Federated Learning (FL) paves the path towards preserving data privacy, performing distributed learning, and reducing communication overhead in large-scale machine learning (ML) problems. This paper proposes an FL model by monitoring client activities and leveraging available local computing resources, particularly for resource-constrained IoT devices (e.g., mobile robots), to accelerate the learning process. We assign a trust score to each FL client, which is updated based on the client's activities. We consider a distributed mobile robot as an FL client with resource limitations either in memory, bandwidth, processor, or battery life. We consider such mobile robots as FL clients to understand their resource-constrained behavior in a real-world setting. We consider an FL client to be untrustworthy if the client infuses incorrect models or repeatedly gives slow responses during the FL process. After disregarding the ineffective and unreliable client, we perform local training on the selected FL clients. To further reduce the straggler issue, we enable an asynchronous FL mechanism by performing aggregation on the FL server without waiting for a long period to receive a particular client's response.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/icmla51294.2020.00185
- OA Status
- green
- Cited By
- 1
- References
- 47
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3118510992
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3118510992Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icmla51294.2020.00185Digital Object Identifier
- Title
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FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile RobotsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-01Full publication date if available
- Authors
-
Ahmed Imteaj, M. Hadi AminiList of authors in order
- Landing page
-
https://doi.org/10.1109/icmla51294.2020.00185Publisher 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/2101.03705.pdfDirect OA link when available
- Concepts
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Computer science, Asynchronous communication, Distributed computing, Overhead (engineering), Robot, Mobile robot, The Internet, Artificial intelligence, Computer network, World Wide Web, Operating systemTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
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47Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mobile | 112, 140, 160 |
| abstract_inverted_index.models | 187 |
| abstract_inverted_index.period | 237 |
| abstract_inverted_index.reduce | 215 |
| abstract_inverted_index.robots | 161 |
| abstract_inverted_index.server | 231 |
| abstract_inverted_index.source | 13 |
| abstract_inverted_index.Machine | 62 |
| abstract_inverted_index.battery | 155 |
| abstract_inverted_index.client, | 127, 203 |
| abstract_inverted_index.clients | 164 |
| abstract_inverted_index.devices | 7, 32, 110 |
| abstract_inverted_index.further | 214 |
| abstract_inverted_index.infuses | 185 |
| abstract_inverted_index.locally | 40 |
| abstract_inverted_index.machine | 86 |
| abstract_inverted_index.memory, | 151 |
| abstract_inverted_index.opposed | 48 |
| abstract_inverted_index.perform | 42, 205 |
| abstract_inverted_index.primary | 11 |
| abstract_inverted_index.receive | 239 |
| abstract_inverted_index.towards | 73 |
| abstract_inverted_index.updated | 130 |
| abstract_inverted_index.waiting | 233 |
| abstract_inverted_index.without | 232 |
| abstract_inverted_index.Learning | 63, 68 |
| abstract_inverted_index.behavior | 169 |
| abstract_inverted_index.client's | 134, 242 |
| abstract_inverted_index.clients. | 212 |
| abstract_inverted_index.consider | 137, 158, 175 |
| abstract_inverted_index.internet | 24 |
| abstract_inverted_index.learning | 87, 117 |
| abstract_inverted_index.network. | 17 |
| abstract_inverted_index.overhead | 83 |
| abstract_inverted_index.privacy, | 76 |
| abstract_inverted_index.process. | 118, 196 |
| abstract_inverted_index.proposed | 61 |
| abstract_inverted_index.proposes | 92 |
| abstract_inverted_index.recently | 60 |
| abstract_inverted_index.reducing | 81 |
| abstract_inverted_index.resource | 147 |
| abstract_inverted_index.robots), | 113 |
| abstract_inverted_index.selected | 210 |
| abstract_inverted_index.setting. | 173 |
| abstract_inverted_index.training | 207 |
| abstract_inverted_index.Federated | 67 |
| abstract_inverted_index.algorithm | 65 |
| abstract_inverted_index.available | 102 |
| abstract_inverted_index.computing | 104 |
| abstract_inverted_index.desirable | 36 |
| abstract_inverted_index.incorrect | 186 |
| abstract_inverted_index.learning, | 79 |
| abstract_inverted_index.mechanism | 224 |
| abstract_inverted_index.problems. | 89 |
| abstract_inverted_index.response. | 243 |
| abstract_inverted_index.responses | 192 |
| abstract_inverted_index.straggler | 217 |
| abstract_inverted_index.vehicles, | 2 |
| abstract_inverted_index.accelerate | 115 |
| abstract_inverted_index.activities | 99 |
| abstract_inverted_index.autonomous | 1 |
| abstract_inverted_index.bandwidth, | 152 |
| abstract_inverted_index.considered | 9 |
| abstract_inverted_index.continuous | 27 |
| abstract_inverted_index.leveraging | 101 |
| abstract_inverted_index.monitoring | 97 |
| abstract_inverted_index.particular | 241 |
| abstract_inverted_index.performing | 77, 226 |
| abstract_inverted_index.preserving | 74 |
| abstract_inverted_index.processor, | 153 |
| abstract_inverted_index.real-world | 172 |
| abstract_inverted_index.repeatedly | 189 |
| abstract_inverted_index.resources, | 105 |
| abstract_inverted_index.understand | 166 |
| abstract_inverted_index.unreliable | 202 |
| abstract_inverted_index.activities. | 135 |
| abstract_inverted_index.aggregation | 227 |
| abstract_inverted_index.centralized | 56 |
| abstract_inverted_index.computation | 43, 57 |
| abstract_inverted_index.distributed | 16, 78, 139 |
| abstract_inverted_index.improvement | 28 |
| abstract_inverted_index.ineffective | 200 |
| abstract_inverted_index.information | 53 |
| abstract_inverted_index.large-scale | 85 |
| abstract_inverted_index.limitations | 148 |
| abstract_inverted_index.Smartphones, | 0 |
| abstract_inverted_index.asynchronous | 222 |
| abstract_inverted_index.availability | 25 |
| abstract_inverted_index.breakthrough | 22 |
| abstract_inverted_index.disregarding | 198 |
| abstract_inverted_index.particularly | 106 |
| abstract_inverted_index.capabilities, | 33 |
| abstract_inverted_index.communication | 82 |
| abstract_inverted_index.revolutionary | 21 |
| abstract_inverted_index.untrustworthy | 181 |
| abstract_inverted_index.Internet-of-things | 5 |
| abstract_inverted_index.resource-constrained | 108, 168 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.58218738 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |