Edge-Cloud Computing and Federated-Split Learning in the Internet of Things
Federated Learning (FL) is a new collaborative learning method that allows multiple data owners to cooperate in ML model training without exposing private data. Split Learning (SL) is an emerging collaborative learning method that splits an ML model into multiple portions that are trained collaborat...
Sábháilte in:
| Formáid: | Online |
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| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20250220_9783725819942_64 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869527060233322496 |
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| collection | Directory of Open Access Books |
| description | Federated Learning (FL) is a new collaborative learning method that allows multiple data owners to cooperate in ML model training without exposing private data. Split Learning (SL) is an emerging collaborative learning method that splits an ML model into multiple portions that are trained collaboratively by different entities. FL and SL, each have unique advantages and respective limitations, may complement each other to facilitate effective collaborative learning in the Internet of Things (IoT). The rapid development of edge-cloud computing technologies enables a distributed platform upon which the FL and SL frameworks can be deployed. Therefore, FL and SL deployed upon an edge-cloud platform in an IoT environment have formed an active research area that attracts interest from both academia and industry. This reprint of the special issue “Edge-Cloud Computing and Federated-Split Learning in the Internet of Things” aims to present the latest research advances in this interdisciplinary field of edge-cloud computing and federated-split learning. This special issue includes twelve research articles that address various aspects of edge-cloud computing and federated-split learning, including technologies for improving the performance and efficiency of FL and SL in edge-cloud computing environments, mechanisms for protecting the data privacy and system security in FL and SL frameworks, and exploitation of FL/SL-based ML methods together with edge/cloud computing technologies for supporting various IoT applications. |
| format | Online |
| id | doab-20.500.12854ir-152700 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1527002025-02-20T12:56:03Z Edge-Cloud Computing and Federated-Split Learning in the Internet of Things Duan, Qiang Lu, Zhihui federated learning Internet of Things clustering communication efficiency convolutional neural network machine learning edge computing edge cloud OFD files semantic analysis dynamic watermarking differential privacy homomorphic encryption privacy accuracy client selection model aggregation semi-synchronous IoT edge cloud computing Internet of things dingo optimization algorithm salp swarm algorithm transfer learning virtual machines raspberry PI proof-of-concept Federated Learning (FL) intrusion detection systems (IDS) Internet of Vehicles (IoV) deep learning image classification disease risk prediction split learning split federated learning artificial intelligent internet of things sensitive data classification and grading augmentation synonym mining financial scenarios privacy-preserving blockchain n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology Federated Learning (FL) is a new collaborative learning method that allows multiple data owners to cooperate in ML model training without exposing private data. Split Learning (SL) is an emerging collaborative learning method that splits an ML model into multiple portions that are trained collaboratively by different entities. FL and SL, each have unique advantages and respective limitations, may complement each other to facilitate effective collaborative learning in the Internet of Things (IoT). The rapid development of edge-cloud computing technologies enables a distributed platform upon which the FL and SL frameworks can be deployed. Therefore, FL and SL deployed upon an edge-cloud platform in an IoT environment have formed an active research area that attracts interest from both academia and industry. This reprint of the special issue “Edge-Cloud Computing and Federated-Split Learning in the Internet of Things” aims to present the latest research advances in this interdisciplinary field of edge-cloud computing and federated-split learning. This special issue includes twelve research articles that address various aspects of edge-cloud computing and federated-split learning, including technologies for improving the performance and efficiency of FL and SL in edge-cloud computing environments, mechanisms for protecting the data privacy and system security in FL and SL frameworks, and exploitation of FL/SL-based ML methods together with edge/cloud computing technologies for supporting various IoT applications. 2025-02-20T12:56:00Z 2025-02-20T12:56:00Z 2024 book ONIX_20250220_9783725819942_64 9783725819942 9783725819935 https://directory.doabooks.org/handle/20.500.12854/152700 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/9862 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-1993-5 10.3390/books978-3-7258-1993-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725819942 9783725819935 294 Basel open access |
| spellingShingle | federated learning Internet of Things clustering communication efficiency convolutional neural network machine learning edge computing edge cloud OFD files semantic analysis dynamic watermarking differential privacy homomorphic encryption privacy accuracy client selection model aggregation semi-synchronous IoT edge cloud computing Internet of things dingo optimization algorithm salp swarm algorithm transfer learning virtual machines raspberry PI proof-of-concept Federated Learning (FL) intrusion detection systems (IDS) Internet of Vehicles (IoV) deep learning image classification disease risk prediction split learning split federated learning artificial intelligent internet of things sensitive data classification and grading augmentation synonym mining financial scenarios privacy-preserving blockchain n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology Edge-Cloud Computing and Federated-Split Learning in the Internet of Things |
| title | Edge-Cloud Computing and Federated-Split Learning in the Internet of Things |
| title_full | Edge-Cloud Computing and Federated-Split Learning in the Internet of Things |
| title_fullStr | Edge-Cloud Computing and Federated-Split Learning in the Internet of Things |
| title_full_unstemmed | Edge-Cloud Computing and Federated-Split Learning in the Internet of Things |
| title_short | Edge-Cloud Computing and Federated-Split Learning in the Internet of Things |
| title_sort | edge cloud computing and federated split learning in the internet of things |
| topic | federated learning Internet of Things clustering communication efficiency convolutional neural network machine learning edge computing edge cloud OFD files semantic analysis dynamic watermarking differential privacy homomorphic encryption privacy accuracy client selection model aggregation semi-synchronous IoT edge cloud computing Internet of things dingo optimization algorithm salp swarm algorithm transfer learning virtual machines raspberry PI proof-of-concept Federated Learning (FL) intrusion detection systems (IDS) Internet of Vehicles (IoV) deep learning image classification disease risk prediction split learning split federated learning artificial intelligent internet of things sensitive data classification and grading augmentation synonym mining financial scenarios privacy-preserving blockchain n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology |
| topic_facet | federated learning Internet of Things clustering communication efficiency convolutional neural network machine learning edge computing edge cloud OFD files semantic analysis dynamic watermarking differential privacy homomorphic encryption privacy accuracy client selection model aggregation semi-synchronous IoT edge cloud computing Internet of things dingo optimization algorithm salp swarm algorithm transfer learning virtual machines raspberry PI proof-of-concept Federated Learning (FL) intrusion detection systems (IDS) Internet of Vehicles (IoV) deep learning image classification disease risk prediction split learning split federated learning artificial intelligent internet of things sensitive data classification and grading augmentation synonym mining financial scenarios privacy-preserving blockchain n/a thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology |
| url | ONIX_20250220_9783725819942_64 |