Abstract
Abstract Securing medical records is a significant task in Healthcare communication. The major setback during the transfer of medical data in the electronic medium is the inherent difficulty in preserving data confidentiality and patients’ privacy. The innovation in technology and improvisation in the medical field has given numerous advancements in transferring the medical data with foolproof security. In today’s healthcare industry, federated network operation is gaining significance to deal with distributed network resources due to the efficient handling of privacy issues. The design of a federated security system for healthcare services is one of the intense research topics. This article highlights the importance of federated learning in healthcare. Also, the article discusses the privacy and security issues in communicating the e-health data.
Highlights
Federated learning (FL) is a shared learning model that works without exchanging users’ original data
Consensus solution and pluralistic solution are recommended to overcome the statistical challenge of FL
Cryptographic techniques are applied for Electronic Health Record (EHR) systems to provide safe transmission using the public key infrastructure
Summary
Federated learning (FL) is a shared learning model that works without exchanging users’ original data. Traditional machine learning involves a centralized approach which requires the training data to be aggregated on a single machine or in a data center. Such data collected in a specific environment need to be communicated back to the central server. Each participant updates local model parameters by using the global model The server does the grouping process of local models, and updated global model parameters are sent to data owners. Traditional data processing models have become ineffective to address the security and privacy issues.
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