Privacy-Preserving and Explainable Federated Clinical Intelligence Using Secure Aggregation, Differential Privacy and Multimodal XAI
Keywords:
federated learning, secure aggregation, differential privacy, explainable AI, multimodal clinical AI, calibration, privacy–utility trade-off, healthcare decision support.Abstract
Federated clinical artificial intelligence reduces the need to centralize sensitive patient records, but datalocality alone does not guarantee privacy, secure collaboration, calibrated confidence, or interpretable predictions. This paper presents a trustworthiness-focused extension of a multimodal clinical intelligence
References
[1] K. Bonawitz et al., “Practical secure aggregation for privacy-preserving machine learning,” Proc. ACM SIGSAC Conf. Computer and Communications Security (CCS), pp. 1175–1191, 2017. doi:10.1145/3133956.3133982
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Published
2024-10-15
How to Cite
Maria Archana Devi.P ,Dr. Gurvinder Singh. (2024). Privacy-Preserving and Explainable Federated Clinical Intelligence Using Secure Aggregation, Differential Privacy and Multimodal XAI . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7919–7927. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5863
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