A Distributed Artificial Intelligence Framework with Federated Machine Learning for Privacy-Preserving Healthcare Data Analytics in Multi-Cloud Environments

Abstract

The quick development of online health systems and smart healthcare applications has produced huge amounts of sensitive patient data that must be analyzed using secure and scalable analytics and privacy-protective frameworks. The old centralized artificial intelligence models are usually based on transfer of healthcare data to one cloud server where data privacy and security vulnerabilities, regulatory compliance, and single point of failure issues are problematic. The research aims at overcoming these drawbacks by proposing a Distributed Artificial Intelligence Framework that is combined with Federated Machine Learning to conduct Privacy-Preserving Healthcare Data Analytics in Multi-Cloud Environments. The suggested model will use a decentralized collaborative learning algorithm, called Federated Averaging (FedAvg) which allows many healthcare institutions to cooperate without exchanging raw patient records. Patient confidentiality and minimizing privacy exposure are ensured by the local healthcare datasets being trained at distributed cloud nodes, with only model parameters being communicated to a federated server and aggregated, and no information shared. The multi-cloud deployment architecture helps boost the scalability, computation effectiveness, reliability, and distributed resource management of heterogeneous healthcare infrastructure. The use of healthcare analytical datasets under distributed federated settings and the Accuracy metric was used in assessing system performance to conduct experimental evaluation. The results retrieved prove that the suggested FedAvg-based distributed scheme is characterized by a high predictive accuracy and, on the other hand, ensures data privacy and preserves central dependence of data. In addition, the framework enhances teamwork healthcare intelligence, offers secure distributed medical analytics and provides a scalable solution to next-generation intelligent healthcare systems. The paper underscores the real-world relevance of federated distributed AI towards facilitating secure, efficient, and privacy conscious healthcare analytics in the modern multi-cloud computing environment.