PrivateBoost: Privacy-Preserving Federated Gradient Boosting for Cross-Device Medical Data

2026-02-12 health informatics Title + abstract only

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Cross-device medical federated learning--where individual patients participate directly rather than institutions--poses a unique challenge: each client holds only a few samples, often just one (e.g., a single diagnostic record), leaving insufficient local data for gradient computation. Existing approaches, such as Secure Aggregation, require client-to-client coordination impractical for intermittently available mobile devices, while homomorphic encryption introduces substantial computational ove...

PrivateBoost: Privacy-Preserving Federated Gradient Boosting for Cross-Device Medical Data

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