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

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-based alternatives introduce sophisticated key management and coordination requirements ill-suited to dynamic cross-device deployments. We present \textit{privateboost}, a federated XGBoost system that addresses this setting through m-of-n Shamir secret sharing with commitment-based anonymous aggregation. Clients distribute shares to a fixed set of shareholders requiring no client-to-client communication and the aggregator reconstructs only aggregate gradient sums via Lagrange interpolation, never observing individual values or client identities. We evaluate on UCI medical datasets, demonstrating 98\% split gain retention relative to centralized XGBoost and accuracy resilient to up to 80\% client dropout.