Edge-Cloud Computing and Federated-Split Learning in the Internet of Things

Edge-Cloud Computing and Federated-Split Learning in the Internet of Things

Computer Science

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.

Qiang Duan,
Zhihui Lu

https://mdpi-res.com/bookfiles/book/9862/EdgeCloud_Computing_and_FederatedSplit_Learning_in_the_Internet_of_Things.pdf?v=1768788667

MDPI - Multidisciplinary Digital Publishing Institute

2024

Mustabsyirah

Creative Commons

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English

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