Federated Learning for Secure and Privacy-Preserving Medical Collaboration Across Multi-Cloud Healthcare Systems

Seamless artificial intelligence (AI) adoption into the contemporary medical frameworks has unlocked new opportunities in medical diagnosis, predictive analysis, and individualistic treatment planning. Nevertheless, the effectiveness of AI models largely depends on getting access to large, diverse, and high-quality data sets, which is becoming a challenging goal to achieve because of stricter privacy laws and due to institutional silos, as well as emerging use of multi-cloud systems by healthcare institutions. Aggregated data collection not only adds a risk of data loss but also, in many cases, goes against patient privacy standards stipulated or regulated by acts like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). As studied in this research, Federated Learning (FL) is a decentralized and privacyprotecting paradigm that enables secure medical collaboration through geographically and administratively decentralized healthcare facilities on various cloud platforms with heterogeneous configurations. FL allows several clients (e.g., hospitals, clinics) to use collective computation in training machine learning models, but not sharing raw data, thus maintaining data locality and data confidentiality. We suggest a holistic system capable of coupling FL into a combined solution of complex privacy-preserving frameworks like secure multi-party computation, differential privacy, and homomorphic encryption to offer end-to-end protection against internal and external threats. The proposed paper offers a strong system architecture that could be used in multi-cloud settings, whose issues will include data non-compatibility, communication expense, model convergence, and compliance policies. The proposed approach's performance, scalability, and security are analogously analyzed using real-world medical imaging and electronic health record (EHR) data, providing a thorough collection of experiments. These findings show that the federated model delivers close-to-accuracy with centralized ones, with much less risk involved in centralized data storage and transmission. Moreover, we prove the framework's flexibility with an alternative of different cloud service providers, proving that it can be applied in the real collaborative healthcare ecosystem. To sum up, the present work confirms that federated learning has the potential to become an ever-changing solution to the creation of secure, privacy-preserving, and regulationcompatible AI in multi-cloud healthcare environments, leading to more morally-intelligent and higherperformance medical AI applications