Securing Healthcare AI: Applied Federal Learning

This chapter examines how Federal Learning (FL) might improve healthcare AI system security to meet the pressing need for data privacy and security. We want to see how FL can secure sensitive health data and build strong AI models using decentralized data sources. The chapter examines healthcare FL implementation and efficacy using previous literature and case studies. FL decreases privacy hazards by storing patient data locally, reducing breaches and unauthorized access. FL’s cooperative nature allows the building highly accurate artificial intelligence models without centralized data storage, which is often attacked. The findings show that healthcare companies may use FL to build secure, scalable AI systems that meet tight data protection criteria. FL promotes medical research innovation and patient outcomes by enabling institution-wide interaction. FL lets healthcare providers improve AI while maintaining data security and patient privacy.