Federated and Edge Learning for Real-Time Data Analytics in Clinical Environments
DOI:
https://doi.org/10.70445/gjus.1.1.2024.246-272Keywords:
Federated learning, Edge learning, Real-time healthcare analytics, Clinical environments, Data privacy, Medical imaging, Artificial intelligence in healthcareAbstract
The new machine learning paradigms that allow privacy-guaranteed real-time data analytics in clinical settings are federated and edge learning. As electronic health records, wearable devices, and Internet of Things continue to multiply the volume of healthcare data, the conventional centralized data management methods are under a challenge due to privacy, latency, and scaling. In federated learning, collaborative model training is possible without exchanging raw patient information, whereas edge learning has the data processed on devices in real-time to provide a faster response. Their integration enables efficient, secure and scalable healthcare systems to be used in applications like remote monitoring, in ICUs and in medical diagnostics. These technologies also have issues such as communication overhead and resource constraints, however, it does not mean that they lack promise of improved patient care.
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