AI and Federated Machine Learning for Smart Healthcare

By: Y. Liu

Due to the development of digital technologies, there is a revolution in the field of federated machine learning and multimedia-based healthcare (MMH).  Now all the important documents like lab reports, diagnoses, etc., of patients are converted into multimedia files. These multimedia files are accessed by the patients from any place, hence MMH removes the geographical restriction. MMH improves the healthcare facilities because now patients can consult doctors at any time and transfer their medical reports to any place. However, the healthcare multimedia documents contain personal and private information of the patients that can be stolen by malicious users during transmission. So there is a need for proper security techniques that can provide confidentiality, integrity, availability of the healthcare multimedia records.

The use of AI and federated machine learning techniques can be used to provide the security and privacy of multimedia healthcare data. Federated learning is a decade-old technology used to provide privacy and integrity to the stored data. In federated learning, the local nodes that stored the private information are trained locally, hence there is no need of sharing the personal and private information of the patients. Federated learning with AI makes the MMH system free from malicious activities.

FAQ on this topic

What is federated machine learning?

Federated machine learning is about training a model or an algorithm over a dataset across decentralized edge devices in distributed networks

Federated learning is secure or not?

Yes, federated learning is secure, because the federated learning dataset is processed at local servers.

What are the applications of federated learning?

Federated learning is used for smart healthcare, Secure gradient sharing

Cite this article

Y. Liu (2021), AI and Federated Machine Learning for Smart Healthcare, Insights2Tecinfo, pp.1

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