Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations

• URL: http://arxiv.org/abs/2207.07700v1
• Date: Fri, 15 Jul 2022 18:48:57 GMT
• Title: Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations
• Authors: Karolina Bogacka, Katarzyna Wasielewska-Michniewska, Marcin Paprzycki, Maria Ganzha, Anastasiya Danilenka, Lambis Tassakos, Eduardo Garro
• Abstract summary: Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training.
• Score: 0.31402652384742363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training. Until now, the majority of research has been devoted to "core issues", such as adaptation of machine learning algorithms to FL, data privacy protection, or dealing with the effects of uneven data distribution between clients. This contribution is anchored in a practical use case, where FL is to be actually deployed within an Internet of Things ecosystem. Hence, somewhat different issues that need to be considered, beyond popular considerations found in the literature, are identified. Moreover, an architecture that enables the building of flexible, and adaptable, FL solutions is introduced.

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