TinyFL: On-Device Training, Communication and Aggregation on a Microcontroller For Federated Learning

Wulfert, Lars; Wiede, Christian; Grabmaier, Anton

doi:10.1109/newcas57931.2023.10198040

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…[50] A promising avenue for future research involves researching into the realm of multi-centerFL [51] to effectively tackle the complexities brought about by this heterogeneity. [52] 4…”

Section: Resolve Heterogeneity Issuementioning

confidence: 99%

Federated Learning for Predictive Healthcare Analytics: From theory to real world applications

Rana,

Marwaha

2024

BIO Web Conf.

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In the contemporary landscape, machine learning has a pervasive impact across virtually all industries. However, the success of these systems hinges on the accessibility of training data. In today's world, every device generates data, which can serve as the building blocks for future technologies. Conventional machine learning methods rely on centralized data for training, but the availability of sufficient and valid data is often hindered by privacy concerns. Data privacy is the main concern while developing a healthcare system. One of the technique which allow decentralized learning is Federated Learning. Researchers have been actively applying this approach in various domains and have received a positive response. This paper underscores the significance of employing Federated Learning in the healthcare sector, emphasizing the wealth of data present in hospitals and electronic health records that could be used to train medical systems.

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“…[50] A promising avenue for future research involves researching into the realm of multi-centerFL [51] to effectively tackle the complexities brought about by this heterogeneity. [52] 4…”

Section: Resolve Heterogeneity Issuementioning

confidence: 99%