Enhancing Healthcare through Federated Learning: Privacy, Security, and Performance

Federated learning has emerged as a transformative technique in the fields of AI and healthcare, offering a potential solution to harness data from diverse sources while safeguarding patient privacy. Despite its promise, federated learning encounters unique challenges: data quality inconsistencies, vertically dispersed datasets among healthcare providers, and datasets with minimal sample sizes, often featuring rare subgroups. Centralized methods struggle to address these concerns, particularly when data sharing is restricted across national borders. Privacy-enhancement technologies (PETs) such as secure multiparty computation and differential privacy offer complementary strategies, yet innovative approaches are necessary to navigate data reuse challenges effectively. Additionally, federated learning systems, despite their protective design, remain vulnerable to malicious attacks, necessitating ongoing discussions about system vulnerabilities and defensive measures.

This Research Topic aims to delve into the nuances of federated learning in healthcare AI, focusing on amalgamating PETs with federated learning algorithms to derive insights from private datasets belonging to healthcare institutions. Key objectives include: evaluating the effectiveness of these technologies on healthcare, socio-economic, psycho-social, and multi-omics data, and examining long-term health outcomes. A particular emphasis is placed on understanding the risks associated with potential attacks, assessing mitigation strategy effectiveness, and achieving sustainable federated learning implementation. Through the integration of federated learning within AI healthcare applications, this Topic seeks to balance data protection with performance, enhancing federation techniques and crafting secure, effective machine learning models that personalize and improve patient care.

To gather further insights into federated learning applications in healthcare AI, we welcome articles addressing, but not limited to, the following themes:

- Implementation of federated training for GenAI models in language and vision processing
- Collaborative use of generative models and synthetic data creation
- Ethical, legal, and societal implications (ELSI) of federated systems, with a focus on one review/secondary research paper
- Methodologies for federated learning with vertical partitioning, small datasets, and rare disease conditions
- Data quality management and bias detection/mitigation in federated data

We encourage original research papers that address these areas, aiming to foster advancements in federated learning and improve outcomes in the healthcare sector.