A Federated Learning Framework for Healthcare IoT devices Article Swipe
Binhang Yuan
,
Ge Song
,
Wenhui Xing
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.05083
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2005.05083
The Internet of Things (IoT) revolution has shown potential to give rise to many medical applications with access to large volumes of healthcare data collected by IoT devices. However, the increasing demand for healthcare data privacy and security makes each IoT device an isolated island of data. Further, the limited computation and communication capacity of wearable healthcare devices restrict the application of vanilla federated learning. To this end, we propose an advanced federated learning framework to train deep neural networks, where the network is partitioned and allocated to IoT devices and a centralized server. Then most of the training computation is handled by the powerful server. The sparsification of activations and gradients significantly reduces the communication overhead. Empirical study have suggested that the proposed framework guarantees a low accuracy loss, while only requiring 0.2% of the synchronization traffic in vanilla federated learning.
Related Topics
Concepts
Computer science
Federated learning
Overhead (engineering)
Internet of Things
Deep learning
Wearable computer
Computation
Health care
Server
Synchronization (alternating current)
Computer network
Artificial intelligence
Distributed computing
Computer security
Embedded system
Channel (broadcasting)
Economics
Algorithm
Operating system
Economic growth
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2005.05083
- https://arxiv.org/pdf/2005.05083
- OA Status
- green
- Cited By
- 49
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3022195403
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3022195403Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2005.05083Digital Object Identifier
- Title
-
A Federated Learning Framework for Healthcare IoT devicesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-07Full publication date if available
- Authors
-
Binhang Yuan, Ge Song, Wenhui XingList of authors in order
- Landing page
-
https://arxiv.org/abs/2005.05083Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2005.05083Direct link to full text PDF
- 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/2005.05083Direct OA link when available
- Concepts
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Computer science, Federated learning, Overhead (engineering), Internet of Things, Deep learning, Wearable computer, Computation, Health care, Server, Synchronization (alternating current), Computer network, Artificial intelligence, Distributed computing, Computer security, Embedded system, Channel (broadcasting), Economics, Algorithm, Operating system, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
49Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 10, 2023: 12, 2022: 11, 2021: 6Per-year citation counts (last 5 years)
- References (count)
-
14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.network | 82 |
| abstract_inverted_index.privacy | 35 |
| abstract_inverted_index.propose | 69 |
| abstract_inverted_index.reduces | 113 |
| abstract_inverted_index.server. | 93, 105 |
| abstract_inverted_index.traffic | 137 |
| abstract_inverted_index.vanilla | 62, 139 |
| abstract_inverted_index.volumes | 20 |
| abstract_inverted_index.Further, | 47 |
| abstract_inverted_index.However, | 28 |
| abstract_inverted_index.Internet | 1 |
| abstract_inverted_index.accuracy | 128 |
| abstract_inverted_index.advanced | 71 |
| abstract_inverted_index.capacity | 53 |
| abstract_inverted_index.devices. | 27 |
| abstract_inverted_index.isolated | 43 |
| abstract_inverted_index.learning | 73 |
| abstract_inverted_index.powerful | 104 |
| abstract_inverted_index.proposed | 123 |
| abstract_inverted_index.restrict | 58 |
| abstract_inverted_index.security | 37 |
| abstract_inverted_index.training | 98 |
| abstract_inverted_index.wearable | 55 |
| abstract_inverted_index.Empirical | 117 |
| abstract_inverted_index.allocated | 86 |
| abstract_inverted_index.collected | 24 |
| abstract_inverted_index.federated | 63, 72, 140 |
| abstract_inverted_index.framework | 74, 124 |
| abstract_inverted_index.gradients | 111 |
| abstract_inverted_index.learning. | 64, 141 |
| abstract_inverted_index.networks, | 79 |
| abstract_inverted_index.overhead. | 116 |
| abstract_inverted_index.potential | 8 |
| abstract_inverted_index.requiring | 132 |
| abstract_inverted_index.suggested | 120 |
| abstract_inverted_index.guarantees | 125 |
| abstract_inverted_index.healthcare | 22, 33, 56 |
| abstract_inverted_index.increasing | 30 |
| abstract_inverted_index.revolution | 5 |
| abstract_inverted_index.activations | 109 |
| abstract_inverted_index.application | 60 |
| abstract_inverted_index.centralized | 92 |
| abstract_inverted_index.computation | 50, 99 |
| abstract_inverted_index.partitioned | 84 |
| abstract_inverted_index.applications | 15 |
| abstract_inverted_index.communication | 52, 115 |
| abstract_inverted_index.significantly | 112 |
| abstract_inverted_index.sparsification | 107 |
| abstract_inverted_index.synchronization | 136 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |