Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art Article Swipe
Ahmed Imteaj
,
Urmish Thakker
,
Shiqiang Wang
,
Jian Li
,
M. Hadi Amini
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.10610
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2002.10610
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities. Further, widespread Internet availability enables communication among sensing devices. As a result, vast amounts of data are generated on edge devices to drive Internet-of-Things (IoT), crowdsourcing, and other emerging technologies. The collected extensive data can be pre-processed, scaled, classified, and finally, used for predicting future events using machine learning (ML) methods. In traditional ML approaches, data is sent to and processed in a central server, which encounters communication overhead, processing delay, privacy leakage, and security issues. To overcome these challenges, each client can be trained locally based on its available data and by learning from the global model. This decentralized learning structure is referred to as Federated Learning (FL). However, in large-scale networks, there may be clients with varying computational resource capabilities. This may lead to implementation and scalability challenges for FL techniques. In this paper, we first introduce some recently implemented real-life applications of FL. We then emphasize on the core challenges of implementing the FL algorithms from the perspective of resource limitations (e.g., memory, bandwidth, and energy budget) of client clients. We finally discuss open issues associated with FL and highlight future directions in the FL area concerning resource-constrained devices.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2002.10610
- https://arxiv.org/pdf/2002.10610
- OA Status
- green
- Cited By
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287864999
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287864999Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2002.10610Digital Object Identifier
- Title
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Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-artWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-02-25Full publication date if available
- Authors
-
Ahmed Imteaj, Urmish Thakker, Shiqiang Wang, Jian Li, M. Hadi AminiList of authors in order
- Landing page
-
https://arxiv.org/abs/2002.10610Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2002.10610Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2002.10610Direct OA link when available
- Concepts
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Computer science, Scalability, Overhead (engineering), Edge device, Distributed computing, Resource (disambiguation), Artificial intelligence, Data science, Machine learning, Database, Cloud computing, Computer network, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 3, 2022: 7, 2021: 4, 2020: 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.security | 85 |
| abstract_inverted_index.Federated | 117 |
| abstract_inverted_index.Nowadays, | 0 |
| abstract_inverted_index.available | 100 |
| abstract_inverted_index.collected | 42 |
| abstract_inverted_index.emphasize | 159 |
| abstract_inverted_index.extensive | 43 |
| abstract_inverted_index.generated | 28 |
| abstract_inverted_index.highlight | 193 |
| abstract_inverted_index.introduce | 149 |
| abstract_inverted_index.networks, | 123 |
| abstract_inverted_index.overhead, | 79 |
| abstract_inverted_index.processed | 71 |
| abstract_inverted_index.real-life | 153 |
| abstract_inverted_index.structure | 112 |
| abstract_inverted_index.algorithms | 168 |
| abstract_inverted_index.associated | 189 |
| abstract_inverted_index.bandwidth, | 177 |
| abstract_inverted_index.challenges | 140, 163 |
| abstract_inverted_index.concerning | 200 |
| abstract_inverted_index.directions | 195 |
| abstract_inverted_index.encounters | 77 |
| abstract_inverted_index.predicting | 54 |
| abstract_inverted_index.processing | 80 |
| abstract_inverted_index.widespread | 12 |
| abstract_inverted_index.approaches, | 65 |
| abstract_inverted_index.challenges, | 90 |
| abstract_inverted_index.classified, | 49 |
| abstract_inverted_index.implemented | 152 |
| abstract_inverted_index.large-scale | 122 |
| abstract_inverted_index.limitations | 174 |
| abstract_inverted_index.perspective | 171 |
| abstract_inverted_index.scalability | 139 |
| abstract_inverted_index.techniques. | 143 |
| abstract_inverted_index.traditional | 63 |
| abstract_inverted_index.applications | 154 |
| abstract_inverted_index.availability | 14 |
| abstract_inverted_index.implementing | 165 |
| abstract_inverted_index.capabilities. | 10, 132 |
| abstract_inverted_index.communication | 16, 78 |
| abstract_inverted_index.computational | 130 |
| abstract_inverted_index.decentralized | 110 |
| abstract_inverted_index.technologies. | 40 |
| abstract_inverted_index.crowdsourcing, | 36 |
| abstract_inverted_index.implementation | 137 |
| abstract_inverted_index.pre-processed, | 47 |
| abstract_inverted_index.Internet-of-Things | 34 |
| abstract_inverted_index.processing/computing | 9 |
| abstract_inverted_index.resource-constrained | 201 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |