Federated Learning for Iot/Edge/Fog Computing Systems
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917594390331392 |
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| author | Hasan, Balqees Talal Idrees, Ali Kadhum |
| author_facet | Hasan, Balqees Talal Idrees, Ali Kadhum |
| contents | With the help of a new architecture called Edge/Fog (E/F) computing, cloud computing services can now be extended nearer to data generator devices. E/F computing in combination with Deep Learning (DL) is a promisedtechnique that is vastly applied in numerous fields. To train their models, data producers in conventional DL architectures with E/F computing enable them to repeatedly transmit and communicate data with third-party servers, like Edge/Fog or cloud servers. Due to the extensive bandwidth needs, legal issues, and privacy risks, this architecture is frequently impractical. Through a centralized server, the models can be co-trained by FL through distributed clients, including cars, hospitals, and mobile phones, while preserving data localization. As it facilitates group learning and model optimization, FL can therefore be seen as a motivating element in the E/F computing paradigm. Although FL applications in E/F computing environments have been considered in previous studies, FL execution and hurdles in the E/F computing framework have not been thoroughly covered. In order to identify advanced solutions, this chapter will provide a review of the application of FL in E/F computing systems. We think that by doing this chapter, researchers will learn more about how E/F computing and FL enable related concepts and technologies. Some case studies about the implementation of federated learning in E/F computing are being investigated. The open issues and future research directions are introduced. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13029 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning for Iot/Edge/Fog Computing Systems Hasan, Balqees Talal Idrees, Ali Kadhum Networking and Internet Architecture With the help of a new architecture called Edge/Fog (E/F) computing, cloud computing services can now be extended nearer to data generator devices. E/F computing in combination with Deep Learning (DL) is a promisedtechnique that is vastly applied in numerous fields. To train their models, data producers in conventional DL architectures with E/F computing enable them to repeatedly transmit and communicate data with third-party servers, like Edge/Fog or cloud servers. Due to the extensive bandwidth needs, legal issues, and privacy risks, this architecture is frequently impractical. Through a centralized server, the models can be co-trained by FL through distributed clients, including cars, hospitals, and mobile phones, while preserving data localization. As it facilitates group learning and model optimization, FL can therefore be seen as a motivating element in the E/F computing paradigm. Although FL applications in E/F computing environments have been considered in previous studies, FL execution and hurdles in the E/F computing framework have not been thoroughly covered. In order to identify advanced solutions, this chapter will provide a review of the application of FL in E/F computing systems. We think that by doing this chapter, researchers will learn more about how E/F computing and FL enable related concepts and technologies. Some case studies about the implementation of federated learning in E/F computing are being investigated. The open issues and future research directions are introduced. |
| title | Federated Learning for Iot/Edge/Fog Computing Systems |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2402.13029 |