Federated Learning in Healthcare: A Chapter from Collaborative Intelligence

(Taylor & Francis, 2024)

I'm excited to share my contribution to the book "Federated Learning: Unlocking the Power of Collaborative Intelligence" (CRC Press, 2024), where I authored the chapter on Federated Learning in Healthcare. This chapter explores the transformative potential of federated learning in the healthcare sector, addressing critical challenges in privacy, security, and collaboration.

Overview of the Chapter

The chapter delves into how machine learning (ML) has become a promising approach for building robust and accurate models driven by medical data. However, due to the sensitive nature of healthcare records, collaboration, security, and privacy have been significant challenges. Federated learning (FL) emerges as a collaborative, privacy-preserving, and secure technique that can address these challenges while maintaining or even improving upon the performance of classical ML models.

Key Topics Covered

• Privacy-Preserving Healthcare Analytics: How FL enables secure analysis of medical records while maintaining patient privacy
• Medical Data Collaboration: Overcoming barriers to healthcare data sharing across institutions
• Clinical Applications: Real-world use cases of FL in medical imaging, diagnosis, and treatment planning
• Global Healthcare Impact: How FL can bring equality and fairness to healthcare delivery
• Future Directions: The path forward for FL-based digital healthcare

Why This Matters

The healthcare sector faces unique challenges when it comes to data privacy and collaboration. Traditional approaches to ML in healthcare often require centralizing sensitive patient data, which raises significant privacy concerns. Federated learning offers a solution that allows healthcare institutions to collaborate on ML models without sharing raw patient data, potentially revolutionizing how we approach healthcare analytics.

Looking Forward

The chapter concludes by discussing the necessary steps to reshape healthcare through FL-based digital systems. This includes addressing technical challenges, establishing trust mechanisms, and ensuring equitable access to healthcare innovations.

You can find this chapter in the book "Federated Learning: Unlocking the Power of Collaborative Intelligence" published by CRC Press in 2024. The book is available through Taylor & Francis.

I'm proud to contribute to this important work that bridges the gap between advanced ML techniques and practical healthcare applications. The chapter represents my commitment to advancing both the technical and practical aspects of federated learning in healthcare.