Federated Learning Analysis in Decentralized Systems
Keywords:
federated learning (FL), AI models, healthcare.Abstract
The growing necessity to protect sensitive health information has led to the rise of federated learning (FL), a distributed architecture for machine learning. Improving the healthcare system becomes necessary during a pandemic. The healthcare industry is continuously making use of numerous AI technology advancements. Because of its decentralized and collaborative approach to constructing AI models, Federated Learning (FL) has gained notice as one such innovation. One of FL's most notable features is that it keeps raw data hidden from prying eyes by keeping it with data sources all the way through training. Because it handles sensitive personal information, FL is more suited to and inevitable in the healthcare industry. Even if there are various privacy and security issues, federated learning (FL) enables multiple institutions to build AI models without sharing data. To be more specific, FL insights can compromise institutional data security. Also, problems can arise with FL when there isn't enough trust between the entities doing the computation. There is an urgent need to clarify the hazards associated with FL because of its increasing use in healthcare. Thus, in this paper, we highlight the literature on privacy-preserving FL as it pertains to healthcare. The risks are highlighted, and methods to lessen them are examined. Researchers in the healthcare industry in Florida can use this review as a resource for information about patient privacy and security in the Sunshine State.
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