In an era where data privacy and accessibility are paramount, federated learning emerges as a transformative paradigm, enabling collaborative AI model training across distributed healthcare datasets. This special collection brings together pioneering research and insights from academia and industry, exploring the intersection of federated with the future of data-driven healthcare. Join us in unravelling the potential of federated learning to revolutionize healthcare delivery while safeguarding patient privacy.
Introduction of the Federated learning in digital healthcare special collection
This collection underscores the profound implications of FL for patient-centric care, emphasizing the pivotal role of individuals in determining the trajectory of their healthcare journey. As we navigate the complexities of digital healthcare in the 21st century, the insights gleaned from this special collection serve as a compass guiding us towards a future where innovation converges with ethics and technology becomes a catalyst for equitable and inclusive healthcare delivery. In embracing the principles of federated learning, we embark on a journey towards a more resilient, responsive, and patient-centric healthcare ecosystem.
Privacy preservation for federated learning in health care
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher’s guide to security and privacy in FL.
Discussion: Emerging trends in federated learning
We will discuss emerging trends of research in federated learning with the collection guest editors, authors and Patterns editors.
