Federated Machine Learning In 5G Smart Healthcare: A Security Perspective Review

Abstract

Federated learning (also known as collaborative learning) is a decentralized approach to training machine learning models. In 5G smart healthcare, federated machine learning (FML) can potentially improve patient care by offering improved diagnosis, prognosis, and therapy models. Nevertheless, a significant worry regarding FML is its lack of security. Within the context of 5G smart healthcare, this review paper looks at FML from a security point of view, discussing the benefits and risks of using FML in 5G smart healthcare and the possible solutions to these risks. The issues of privacy, adversarial attacks, communication security, and malevolent clients are brought up in the discussion on security challenges. Differential privacy, secure aggregation and training, adversarial training, secure communication, client authentication, and model pruning are some of the solutions that have been suggested. We will be able to protect the privacy of patient data in FML if we take the necessary steps to address these security problems.

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