Federated Learning for Privacy Preservation in Healthcare
Abstract
This chapter delves into fundamental concepts of privacy preservation and federated learning (FL) in healthcare. Emphasizing the importance of privacy in healthcare data, it explores ethical and regulatory considerations surrounding sensitive patient information. The history and significance of FL, distinct from traditional centralized machine learning, are discussed, highlighting its relevance in addressing privacy concerns. The limitations of centralized ML are contrasted with FL's advantages, particularly in preserving privacy. Techniques such as FL averaging, aggregation, and secure multi-party computation (SMPC) for privacy-preserving model updates are examined. Real-world examples illustrate their application in healthcare scenarios. The chapter concludes by addressing technical and ethical challenges linked to FL in healthcare, emphasizing its potential to balance patient data protection with AI advancements. Privacy concerns persist in healthcare AI, making FL a promising solution. The discussion extends to emerging trends and potential breakthroughs in this dynamic field.
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