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- W4311597864 abstract "The large volumes of Internet of Things (IoT) data transmission to and from the cloud leads to one of cloud-centric processing’s major drawbacks: latency. Fog computing gives a solution to this by bringing the processing closer to the edge devices. Although a lot of work has been done which makes use of fog nodes like aggregators and offloaders, data intensive tasks like machine learning are still, for the most part, being performed on cloud. In a fog network, there are multiple fog devices, albeit resource-constrained ones. We can make use of distributed processing to collectively utilize these resources. So, we propose CANTO, a general distributed framework which uses the actor model to train neural networks on a network of fog devices in an IoT setting. Since the actors communicate using messages and act according to the type of message they receive, the framework provides a new message type wherein we can specify parameters like the dataset, the size of each dataset part, the activation function, learning rate, etc. The framework is containerized and deployed in a docker swarm. The framework is demonstrated with the use case on IoT-based forest fire prediction. In addition, the effectiveness of the framework is demonstrated with respect to accuracy, latency and load distribution." @default.
- W4311597864 created "2022-12-27" @default.
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- W4311597864 date "2023-02-01" @default.
- W4311597864 modified "2023-09-30" @default.
- W4311597864 title "CANTO: An actor model-based distributed fog framework supporting neural networks training in IoT applications" @default.
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- W4311597864 doi "https://doi.org/10.1016/j.comcom.2022.12.007" @default.
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