Privacy-Preserving Federated Learning for Healthcare Data
Abstract
The evolution of technology has a significant impact on health data collection, transforming the way information is gathered, stored, and utilized in the healthcare industry. The big health record contains sensitive user information like contact details, health status, demographics, vaccination details, exposure history. It's worth noting that while the collection of big health records has been crucial for monitoring the patients' health history, it also raises important privacy and security considerations. Safeguarding the privacy of individuals' health data and ensuring compliance with relevant regulations is essential to maintain public trust and protect sensitive information. Therefore, healthcare data must adhere to privacy regulations and ethical considerations. This chapter elaborates on key challenges and solutions in privacy preservation within federated learning. The key challenges include data heterogeneity, information leakage, attacks, and regulatory compliances.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.