Article

FedXGB-OptDP: A Privacy-Optimised Federated XGBoost Framework with Differential Privacy for IID and Non-IID healthcare data

2026-02-27

Abstract excerpt

<title>Abstract</title> <p>The rapid growth of sensitive healthcare data results in a significant need for machine learning systems capable of providing accurate predictions while safeguarding patient privacy. Due to rapid growth, current privacy-preserving federated tree models face significant computational expenses, inadequate noise allocation methodologies, and losses in accuracy while maintaining a trade-off...

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Literature Corpus work: 966ea428-481a-5677-adb0-3b28a0ce0c0a
DOI: 10.21203/rs.3.rs-8425166/v1

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