Weight-Based Privacy-Preserving Asynchronous SplitFed for Multimedia Healthcare Data

Multimedia significantly enhances modern healthcare by facilitating the analysis and sharing of diverse data, including medical images, videos, and sensor data. Integrating AI for multimedia data classification shows promise in improving healthcare services, data analysis, and decision-making. However, ensuring privacy in AI-integrated healthcare systems remains a challenge, especially with data continuously transmitted over networks. Synchronous Federated Learning (FL) is designed to address these privacy concerns by allowing end devices to collaboratively train a machine learning model without sharing data. Nonetheless, FL alone does not fully resolve privacy issues and faces efficiency challenges, particularly with devices of varying computational capabilities. In this article, we introduce an Asynchronous Partial Privacy-preserving Split-Federated Learning (APP-SplitFed) approach for smart healthcare systems. This method reduces computational demands on resource-limited devices and uses a weight-based aggregation method to allow devices of differing computational power to contribute effectively, ensuring optimal model performance and rapid convergence. Additionally, we incorporate a secure aggregation method to prevent adversaries from identifying individual models owned by healthcare institutions.