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

Abstract

Seamless artificial intelligence ( 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 qua lity 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 d ata loss but also, in many cases, goes against patient privacy
standards stipulated or regulated by acts like the Health Insurance Portability and Accountability Act ( and the General Data
Protection Regulation ( As studied in this research, Fe derated Learning ( is a decentralized and privacy protecting
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. 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 analogo usly analyzed using real world medical imaging and electronic health
record ( 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 invol ved 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 regulation compatible AI in multi cloud healthcare environments, leading
to more morally intelligent and higher performance medical AI applications.