Federated Learning in Cloud Ecosystems: Scalable AI Solutions for Healthcare Security

Abstract

Federated learning (FL) has emerged as a cutting-edge approach for training machine learning models on decentralized data, offering significant advantages for privacy-sensitive sectors such as healthcare. This paper explores the integration of federated learning within cloud ecosystems to develop scalable artificial intelligence (AI) solutions aimed at enhancing healthcare security. By enabling collaborative training across multiple healthcare institutions without sharing sensitive patient data, federated learning mitigates privacy risks while maintaining high model accuracy. The proposed solutions aim to strengthen security frameworks by enhancing predictive analytics for identifying cybersecurity threats, ensuring compliance with healthcare regulations such as HIPAA and GDPR, and safeguarding the integrity of medical data. The paper investigates the technical challenges of deploying federated learning in the cloud, such as data heterogeneity, model convergence, and system scalability, while presenting effective solutions to overcome these challenges. Through real-world case studies, the paper illustrates the potential of federated learning to transform healthcare cybersecurity practices, offering a balance between innovation and regulatory compliance.