Federated Learning in Healthcare: A Privacy Preserving Approach

Area of Science:

• Healthcare technology
• Artificial Intelligence
• Data privacy

Background:

• The COVID-19 pandemic highlighted the need for advanced healthcare solutions.
• Artificial Intelligence (AI) is increasingly integrated into various healthcare fields.
• Federated Learning (FL) offers a decentralized approach to AI model development, crucial for sensitive health data.

Purpose of the Study:

• To explore the concepts and applications of Federated Learning (FL) in the healthcare sector.
• To address the existing privacy vulnerabilities within FL frameworks.
• To propose a novel method for enhancing privacy preservation in healthcare FL.

Main Methods:

• Review of Federated Learning (FL) concepts and current applications in healthcare.
• Identification of privacy threats inherent in FL processes.
• Development and presentation of a new privacy-enhancing technique for FL.

Main Results:

• Federated Learning (FL) enables collaborative AI model training while keeping raw data localized.
• Despite its privacy benefits, FL is susceptible to residual privacy risks.
• A novel approach is proposed to bolster privacy protection in healthcare FL.

Conclusions:

• Federated Learning (FL) is a vital technology for secure AI in healthcare due to its data localization feature.
• Enhanced privacy preservation methods are essential for the widespread adoption of FL in healthcare.
• The presented novel approach offers a pathway to more secure and robust FL implementation in the medical domain.